Hours of research in one daily brief–on your terms.

🔥 TOP SIGNAL

Orchestration is becoming the core dev skill: Addy Osmani argues the enterprise frontier is orchestrating a modest set of agents with control/traceability, not running huge swarms O'Reilly. In practice, that shows up as spec-first work: Brendan Long’s vibe-coding loop starts by writing a detailed GitHub issue ("90% of the work"), optionally having an agent plan, then having another agent implement brendanlong.

🛠️ TOOLS & MODELS

• Codex CLI v0.105 (major QoL upgrade)@thsottiaux

  • New: syntax highlighting, dictate prompts by holding spacebar, better multi-agent workflows, improved approval controls, plus other QoL changes @thsottiaux.
  • Install/upgrade: $ npm i -g @openai/codex@latest@thsottiaux.
  • Practitioner reaction: “diffs are beautiful” and it’s “very, very fast now” @iannuttall.

• Codex app (Windows) — first waitlist batch invited@thsottiaux

  • Team says they’ll “expand from there” as they iterate through feedback @thsottiaux.

• Model preference + benchmarkging signals (Codex 5.3)

  • Mitchell Hashimoto: Codex 5.3 felt “much more effective” than Opus 4.6; after switching back-and-forth, he hasn’t touched Opus for a week @mitchellh.
  • Romain Huet: GPT-5.3-Codex hit 90% on IBench at xhigh reasoning; says with speed gains, “xhigh doesn’t feel like a tradeoff anymore” @romainhuet.
  • Related run: “decided to run 5.3 codex on xhigh as well, its 90%… rip IBench, survived 3 months” @adonis_singh.

• Cursor — Bugbot Autofix (PR issues → auto-fixes)@cursor_ai

  • Announcement: Bugbot can now automatically fix issues it finds in PRs @cursor_ai.
  • Details: http://cursor.com/blog/bugbot-autofix@cursor_ai.

• Devin AI (real production debugging)

  • swyx reports Devin investigated a production bug (Vercel org migration + forgotten key), asked for exactly what it needed, and verified the fix @swyx.

• FactoryAI Droids — “Missions” + terminal “Mission Control”

  • “Missions”: multi-day autonomous goals where you describe what you want, approve a plan, and come back to finished work @FactoryAI +1.
  • Mission Control: a terminal view of which feature is being built, which Droid is on it, tools used, and progress @FactoryAI.
  • Examples FactoryAI says enterprises are running: modernize a 40-year-old COBOL module; migrate >1k microservices across regions; recalc 10 years of pricing; refactor a monolith handling 20M daily API calls with no downtime @matanSF.

• OpenClaw — new beta bits@steipete

  • Adds: external secrets management (openclaw secrets) @steipete +1, CP thread-bound agents @steipete, WebSocket support for Codex @steipete, and Codex/Claude Code as first-class subagents via ACP @steipete.

• Omarchy 3.4 — agent features shipped

  • Release highlights include “new agent features (claude by default + tmux swarm!)” and a tailored tmux setup @dhh +1.

• Harbor framework — shared agent eval infra momentum

  • Laude Institute frames Harbor as shared infrastructure to standardize benchmarks via one interface (repeatable runs, standardized traces, production-grade practice) @LaudeInstitute.
  • swyx says his team is prioritizing migrating evals to Harbor and calls it dominant in RL infra/evals for terminal agents @swyx.

💡 WORKFLOWS & TRICKS

• Spec-driven agent work (make the spec the artifact)

  • Brendan Long’s repeatable loop for large vibe-coded apps:
    1. Write a GitHub issue brendanlong
    2. If it’s complex, have an agent produce a plan and update the issue brendanlong
    3. Have another agent read the issue and implement it brendanlong
  • He claims a detailed enough issue is “90% of the work” and rewriting it is often what fixes problems brendanlong.

• Enterprise-grade orchestration guidance (modest fleets, strong controls)

  • Addy Osmani’s concrete advice: spend 30–40% of task time writing the spec—constraints, success criteria, stack/architecture—and gather context in a resources directory; otherwise you “waste tokens” and LLMs default to “lowest common denominator” patterns O'Reilly.
  • For teams: codify best practices in context (e.g., MCP-callable systems or even markdown files) to raise the odds the output is shippable O'Reilly.
  • He also flags the real bottleneck: “Not generation, but coordination” O'Reilly.

• Close the loop: isolate the runtime so agents can run it

  • Kent C. Dodds: “get your app running in an isolated environment to close the agent loop” @kentcdodds.
  • He points to his Epic Stack guiding principles—“Minimize Setup Friction” and “Offline Development”—as a practical way to make this easier @kentcdodds.

• Hoard working examples, then recombine (prompt with concrete known-good snippets)

  • Simon Willison’s pattern: keep a personal library of solved examples across blogs/TIL, many repos, and small “HTML tools” pages, because agents can recombine them quickly simonwillison.
  • His OCR tool story: he combined snippets for PDF rendering and OCR into a single HTML page via prompt, iterated a few times, and ended with a tool he still uses simonwillison.
  • Agent tip: when asking Claude Code to reuse an existing tool, he sometimes specifies curl explicitly to fetch raw HTML instead of a summarizing fetch tool simonwillison.

• Tests aren’t a moat anymore (agents can recreate them fast)

  • tldraw moved tests to a closed-source repo to prevent “Slop Fork” forks @cramforce.
  • Armin Ronacher’s counterpoint: agents can generate language/implementation-agnostic test suites quickly if there’s a reference implementation @mitsuhiko.

• Security footnote from a vibe-coded app

  • In Simon Willison’s Present.app walkthrough, the remote-control web server used GET requests for state changes (e.g., /next, /prev), which he notes opens up CSRF vulnerabilities—he didn’t care for that application Simon Willison’s Newsletter.

👤 PEOPLE TO WATCH

• Addy Osmani (Google Cloud AI) — clearest “enterprise reality check”: quality bars, traceability, and spec/context discipline, plus a strong stance that orchestration is the thing to learn O'Reilly.
• Simon Willison — consistently turns agent usage into transferable patterns (Agentic Engineering Patterns + “hoard examples” + codebase walkthrough prompts) Simon Willison’s Newsletter +1.
• Brendan Long — practical decomposition: write issues like a system design interview, then let agents execute brendanlong.
• Nicholas Moy (DeepMind) — framing: “10x engineer” becomes “10 agent orchestrator,” measured by concurrent agents you can run effectively @thenickmoy.
• Dylan Patel (Semianalysis) — adoption signal: Claude Code share of GitHub commits going 2%→4% in a month, with a broader estimate of total AI-written code around ~10% Latent Space.

🎬 WATCH & LISTEN

1) Addy Osmani: “Learn orchestration” + the path to agent fleets (≈ 23:32–25:54)

Hook: Practical roadmap from single-agent prompting to multi-agent orchestration and coordination patterns—before you burn tokens on experimental swarms O'Reilly.

2) SAIL LIVE #6: why SWE-Bench got saturated (and what that says about evals) (≈ 29:49–33:48)

Hook: A clear explanation of how SWE-Bench is constructed, why it became the default “agentic coding” benchmark, and why that creates problems once it’s widely known and reused SAIL Media.

📊 PROJECTS & REPOS

• Agentic Engineering Patterns (Simon Willison) — a living guide of coding-agent practices and patterns (agentic engineering vs vibe coding framing) Simon Willison’s Newsletter

  • https://simonwillison.net/guides/agentic-engineering-patterns/Simon Willison’s Newsletter

• Present.app (Simon Willison) — vibe-coded SwiftUI macOS presentation app where each “slide” is a URL; GitHub repo shared Simon Willison’s Newsletter

  • https://github.com/simonw/presentSimon Willison’s Newsletter

• OpenClaw releases + docs (beta features shipping)@steipete

  • https://github.com/openclaw/openclaw/releases@steipete
  • Secrets docs: https://docs.openclaw.ai/cli/secrets@steipete
  • ACP agents docs: https://docs.openclaw.ai/tools/acp-agents@steipete

• Cursor Bugbot Autofix announcement + writeup@cursor_ai

  • http://cursor.com/blog/bugbot-autofix@cursor_ai

• Omarchy 3.4 release (61 contributors; agent features + tmux work) @dhh

  • https://github.com/basecamp/omarchy/releases/tag/v3.4.0@dhh

• tldraw tests move discussion (tests closed-source) @cramforce

  • https://github.com/tldraw/tldraw/issues/8082@cramforce

Editorial take: The leverage is shifting from “pick the best model” to “build the tightest loop”: spec → isolated runtime → tests/evals → approvals—and only then scale agents.

Top Stories

1) Google’s Nano Banana 2 takes #1 in major image leaderboards and rolls out broadly

Why it matters: Image generation is becoming a default capability inside mainstream consumer and developer surfaces; the differentiators are now cost, text rendering, and grounding to real-world conditions via search.

Google DeepMind launched Nano Banana 2 (officially Gemini 3.1 Flash Image Preview) as a state-of-the-art image generation and editing model built on the latest Gemini Flash, aiming for Pro-level capabilities at “Flash” speed@GoogleDeepMind +1. In LMSYS Image Arena, it debuted at #1 and is described as powered by real-time web search information and images@arena.

Key benchmark and pricing callouts:

• Image Arena: #1 Text-to-Image (1279) and ties #1 Single-Image Edit (1407); Top 3 Multi-Image Edit@arena
• Price: $0.067 per image (about 2× cheaper than Nano Banana Pro) @arena
• Text rendering: Image Arena highlighted a 60+ point lead over Nano Banana Pro in text rendering @arena

Google also emphasized the model’s production features (e.g., 512px → 4K upscaling, aspect ratio control, and consistency up to 5 characters + 14 objects) @Google. Rollout includes Gemini App, Search (141 countries), Flow, and preview via AI Studio and Vertex AI @sundarpichai.

2) Anthropic draws explicit red lines in a dispute with the U.S. Department of War

Why it matters: The frontier-lab/government relationship is shifting from abstract “AI policy” debates to contractual demands and enforcement mechanisms.

Anthropic says the Department of War will only contract with AI companies that accede to “any lawful use” and remove safeguards, and that it has threatened to remove Anthropic from military systems, designate it a “supply chain risk,” and invoke the Defense Production Act to force safeguards’ removal @AndrewCurran_.

In the statement, Anthropic describes two excluded use cases:

• Mass domestic surveillance, which it says is “incompatible with democratic values,” and that powerful AI can assemble scattered data into “a comprehensive picture of any person’s life—automatically and at massive scale” @AndrewCurran_.
• Fully autonomous weapons without oversight, arguing today’s frontier AI systems are “not reliable enough” and that proper guardrails “don’t exist today” @AndrewCurran_.

A separate report summarized the stance as refusing to build tools for mass surveillance of U.S. citizens or autonomous weapons without human oversight, despite pressure tied to system access and “supply chain risk” threats @TheRundownAI.

3) Perplexity expands from “search app” to platform integration: Samsung Galaxy S26 + new embedding models

Why it matters: Distribution is moving “down the stack” into OS-level assistants, while retrieval quality becomes a product-level moat.

Perplexity announced it will be built into upcoming Samsung Galaxy S26 devices as a system-level AI with a dedicated wake word (“Hey Plex”), positioned as the first time Samsung has granted OS-level access to a non-Samsung/Google app @perplexity_ai +1. Perplexity says S26 users can launch it via wake word or side button, and that system-level integration enables it to read from and write to Samsung’s core apps directly@AravSrinivas.

Samsung’s Bixby is described as routing complex, web-based, or generative queries to Perplexity APIs, combining real-time web search with LLM reasoning @perplexity_ai +1.

Perplexity also released two embedding model families—pplx-embed-v1 and pplx-embed-context-v1—described as designed for real-world, web-scale retrieval@perplexity_ai. In a separate thread, Perplexity claimed internal web-scale benchmarks with 100K+ real user queries over 1B+ web pages showed strong performance versus competitors @AravSrinivas. Links: https://pplx.ai/pplx-embed@AravSrinivas and quickstart docs https://docs.perplexity.ai/docs/embeddings/quickstart@AravSrinivas.

4) ETH Zurich + Anthropic research highlights low-cost, automated deanonymization at scale

Why it matters: As reasoning and retrieval improve, “anonymous posting” can become fragile—even when no single step looks like deanonymization.

A paper described as “Large-Scale Online Deanonymization with LLMs” demonstrates an automated ESRC pipeline (Extract identity signals → Search via embeddings → Reason → Calibrate), reported to work on platforms including Hacker News and Reddit, with claims of roughly $1 per target @alex_prompter +1. Reported results included 67% correct identification on Hacker News users with 90% precision when guessing@alex_prompter, plus performance on pseudonymous Reddit academics and redacted interviews @alex_prompter.

5) AI-driven capex continues scaling: hyperscaler spending nears half a trillion (2025) with larger projections ahead

Why it matters: Many “model progress” stories are increasingly constrained or enabled by capital deployment (compute, energy, and data center buildout).

Epoch AI Research reported hyperscaler capex driven by AI has grown ~70% per year since GPT-4, nearing $500B total in 2025@EpochAIResearch. If the trend continues, Alphabet, Amazon, Meta, Microsoft, and Oracle could spend $770B on capex in 2026 @EpochAIResearch. Another projection cited ~$800B in 2026 and >$1T per year in 2027 @kimmonismus. Epoch also noted it used a consistent capex measure from financial filings because companies define “capex” differently on earnings calls @EpochAIResearch.

Research & Innovation

Infrastructure + training efficiency: new techniques target bottlenecks across datacenters and inference systems

Why it matters: Many of the biggest capability jumps are increasingly gated by systems-level throughput and training logistics, not only model architecture.

• MuLoCo: A pre-training optimizer positioned as enabling efficient frontier LLM pre-training across datacenters with large enough batch sizes; extends Muon’s advantages to distributed, quantized, and large-scale training, with released code @benjamintherien +1.
• DeepSeek DualPath: Proposes that KV-cache loading “does not have to be prefill-centric,” introducing a storage-to-decode path where KV-cache is loaded into a decode engine first, then transferred to prefill via high-bandwidth RDMA; claims up to 1.87× offline inference throughput and 1.96× higher online agent runs/sec @teortaxesTex +1.

Multi-agent coding efficiency: dynamic topology beats static workflows

Why it matters: As coding agents become standard, orchestration choices can materially change both accuracy and cost.

AgentConductor uses an RL “orchestrator agent” to dynamically generate task-adapted interaction topologies based on inferred agent roles and difficulty @dair_ai. Across five code datasets, it reported up to 14.6% pass@1 improvement, 13% density reduction, and 68% token cost savings @dair_ai. Paper: https://arxiv.org/abs/2602.17100@dair_ai.

World models and shared state: “multiplayer” world modeling in Minecraft

Why it matters: If world models move from “one agent’s view” to persistent shared world state, they can become a substrate for multi-agent coordination.

Project Solaris introduced a multiplayer video world model effort in Minecraft, arguing that “world state is global” and that shared representations beneath individual views are what scale into collective capability @sainingxie. It includes a multiplayer data collection engine, a multiplayer DiT model trained on 12.6M frames of coordinated gameplay, and an evaluation approach using a VLM-as-judge @sainingxie. Project site: https://solaris-wm.github.io/@sainingxie.

Fast, on-the-fly model customization: Doc-to-LoRA and Text-to-LoRA

Why it matters: Techniques that compress “customization” into a fast forward pass can reduce reliance on long prompts or expensive fine-tuning.

Sakana AI Labs introduced Doc-to-LoRA and Text-to-LoRA, using a hypernetwork to generate LoRA adapters on demand to internalize new information or adapt to tasks @SakanaAILabs. They report sub-second latency and released code/papers @SakanaAILabs. Doc-to-LoRA paper: https://arxiv.org/abs/2602.15902@SakanaAILabs; Text-to-LoRA paper: https://arxiv.org/abs/2506.06105@SakanaAILabs.

Interpretability for video world models: V-JEPA 2

Why it matters: Understanding representations used by video models can clarify what they’re actually learning (and what “physics simulation” claims do or don’t mean).

Meta released Interpreting Physics in Video World Models, described as one of the first interpretability studies of video encoders @soniajoseph_. The work suggests modern video models use distributed representations (not factorized variables like a classical physics engine) that are nevertheless sufficient for making physical predictions @raphaelmilliere.

Products & Launches

“Do the work” interfaces: agents that execute multi-step tasks (not just chat)

Why it matters: The UI shift is toward delegation with oversight—describe a goal, review a plan, get finished output.

• Microsoft Copilot Tasks (Research Preview): Positioned as AI that “talks less and does more,” enabling users to delegate work in plain language, review the plan, and stay in control @yusuf_i_mehdi. Example tasks include turning a syllabus into a study plan, tracking apartment listings and booking showings, and triaging emails with draft replies + auto-unsubscribe @mustafasuleyman. Waitlist: https://copilot.microsoft.com/tasks/preview?form=M301EQ&OCID=CGE_osocial_Copilot_Free_868hmzrvb@mustafasuleyman.
• FactoryAI “Missions” for Droids: Described as goal-oriented, multi-day autonomous work—describe what you want, approve the plan, and come back to finished work @FactoryAI. Examples included modernizing a 40-year-old COBOL module and migrating >1k microservices across regions @matanSF.

Developer tooling updates: coding agents and IDE assistance

Why it matters: As more code is generated by agents, teams need better review, debugging, and safe execution environments.

• Claude Code auto-memory: Reported as a new auto-memory feature where Claude remembers project context and preferred approaches across sessions @trq212 +1.
• Cursor Bugbot Autofix: Automatically fixes issues it finds in PRs @cursor_ai. Details: http://cursor.com/blog/bugbot-autofix@cursor_ai.
• VS Code long-distance NES: Next Edit Suggestions can now propose edits anywhere in a file, not only near the cursor @code +1.
• Ollama “Pi”: A minimal coding agent launchable via ollama launch pi, described as customizable and able to write extensions for itself @ollama. Docs: https://docs.ollama.com/integrations/pi@ollama.

New creative and multimodal models shipping to users

Why it matters: Model quality matters, but availability through APIs and platforms determines real adoption.

• PrunaAI P-Video: Launched with claims of fast/cheap video generation (e.g., 10s to generate 5s at 720p; $0.02/s at 720p and $0.04/s at 1080p) @PrunaAI. It entered Video Arena (e.g., #22 Text-to-Video score 1178) @arena.
• Kling 3.0: Released as an all-in-one multimodal creative engine; Kling V3 Pro reached Video Arena top 10 for Image-to-Video (tied #8, score 1337) @Kling_ai +1.
• QuiverAI Arrow 1.0: Public beta for an SVG AI model (“turn your ideas into graphics”) @QuiverAI.

Industry Moves

Capital, partnerships, and major organizational shifts

Why it matters: Distribution, compute access, and enterprise channels increasingly determine who can scale.

• Amazon–OpenAI investment talks: A report claimed Amazon is negotiating a potential $50B investment in OpenAI—$15B upfront plus $35B tied to either an IPO or achieving AGI @kimmonismus +1.
• Sakana AI × Datadog: Strategic partnership focused on enterprise AI innovation and observability, with joint research, potential open-source contributions, and go-to-market efforts @SakanaAILabs. Press release: https://www.datadoghq.com/about/latest-news/press-releases/datadog-sakana-ai-strategic-partnership/@SakanaAILabs.
• Block workforce reduction: Jack Dorsey said Block is reducing headcount from over 10,000 to under 6,000, citing “intelligence tools” enabling smaller, flatter teams and a fundamentally new way of working @jack.
• Thinking Machines Lab departures: Two more founding members of Mira Murati’s Thinking Machines Lab reportedly left for Meta; the startup raised $2B last year @CharlesRollet1.

Policy & Regulation

Defense contracting pressure + “supply chain risk” as a leverage point

Why it matters: “Supply chain risk” designations and Defense Production Act threats represent high-leverage tools that can reshape AI vendor behavior.

Anthropic describes threats including a “supply chain risk” label and potential Defense Production Act invocation to compel safeguard removal @AndrewCurran_. Reporting also described a “best and final” offer for unrestricted military use and possible contract penalties if refused @DeItaone.

A separate thread argued the Department of War has latitude to scope “supply chain risk” narrowly (limiting Claude in sensitive systems without broader business harm) or broadly (potentially forcing contractors to cut ties and raising regulatory risk premia across AI) @deanwball.

Privacy risk from LLM-enabled deanonymization

Why it matters: As ESRC-style pipelines become cheaper, “anonymous” posts may become linkable at scale.

The ETH Zurich + Anthropic paper summary emphasizes that more reasoning compute can improve deanonymization and that the pipeline can be hard to block because it decomposes into benign-looking subtasks (summarize profile, compute embeddings, rank candidates) @alex_prompter.

Quick Takes

Why it matters: Smaller updates often reveal where the ecosystem is hardening—benchmarks, eval tooling, and deployment surfaces.

• Claude Opus 4.6 reached #1 across Text, Code, and Search Arena, with Search Arena score 1255 (+30 over Grok-4.20-beta1, GPT-5.2, and Gemini-3) @arena.
• Code Arena launched a Multi-File React leaderboard to evaluate cross-file coordination, dependency management, state management, and build reliability @arena.
• SWE-bench Multilingual: reported scores slowly moved from ~65% toward ~75%, while average cost-to-solve dropped from $0.67 to $0.10 with Minimax 2.5 @OfirPress.
• Qwen3.5-27B was reported at nearly 50% on Humanity’s Last Exam (HLE) @kimmonismus.
• vLLM 0.16.0 released: https://github.com/vllm-project/vllm/releases/tag/v0.16.0@vllm_project.

What mattered today

Perplexity lands OS-level distribution on Samsung Galaxy S26

Perplexity CEO Aravind Srinivas said Perplexity will be integrated into all Samsung Galaxy S26 phones, preloaded with the wake word “Hey Plex”, alongside Bixby and Gemini @AravSrinivas. He also said Bixby will use Perplexity’s search-grounded LLMs/APIs on the backend for search and reasoning, aiming for “grounded, up-to-date answers” via real-time web search + LLM reasoning @AravSrinivas.

Srinivas described this as system-level integration (wake word, access to physical controls) with the ability to read from and write to native apps like Notes, Calendar, Gallery, Clock, and Reminders @AravSrinivas +1. He also claimed Perplexity is the first non-Google company to have OS-level access in a Samsung phone, and that Samsung is expected to ship hundreds of millions of devices this year with Perplexity powering assistants, browser agents, and search @AravSrinivas +1.

More details were linked here: https://www.perplexity.ai/hub/blog/perplexity-apis-deliver-powerful-ai-to-the-world%E2%80%99s-largest-android-device-maker@AravSrinivas.

Google ships “Nano Banana 2” (Gemini 3.1 Flash Image Preview) with web-search grounding

Multiple Google/DeepMind leaders highlighted the release of Nano Banana 2, officially Gemini 3.1 Flash Image Preview, which is described as being powered by real-time information and images from web search@arena. On Image Arena, it debuted at #1 in Text-to-Image (1279) and tied #1 in Single-Image Edit (1407), with top-3 Multi-Image Edit and a listed cost of $0.067 per image (~2× cheaper than Nano Banana Pro) @arena.

Google also positioned it as rolling out broadly: default in the Gemini app, Search (141 countries), and Flow, plus preview access via AI Studio and Vertex AI@sundarpichai. Sundar Pichai pointed to a “Window Seat” demo that generates window views worldwide using live local weather in 2K/4K specs@sundarpichai.

Agents: more “set a goal, come back later” products

Microsoft: “Copilot Tasks” enters research preview

Microsoft launched Copilot Tasks, positioning it as “AI that talks less and does more,” with no complicated setup or coding—users ask for what they need and Copilot handles the rest @mustafasuleyman. Examples included turning a syllabus into a study plan (with practice tests and blocked focus time), tracking apartment listings and booking showings, and triaging email with drafted replies and auto-unsubscribe actions @mustafasuleyman.

It’s in a Research Preview for a small group, with a waitlist here: https://copilot.microsoft.com/tasks/preview?form=M301EQ&OCID=CGE_osocial_Copilot_Free_868hmzrvb@mustafasuleyman.

FactoryAI: “Missions” for multi-day autonomous work

FactoryAI announced “Missions” for its “Droids,” describing goal-oriented work that can run over multi-day horizons: you describe the goal, approve the plan, and return to finished work @FactoryAI +1. Example targets included large enterprise changes like migrating >1k microservices across regions or refactoring a monolith handling 20M daily API calls without downtime @matanSF.

Perplexity: Nano Banana 2 available inside Perplexity Computer

Separately, Srinivas said Nano Banana 2 is now available for image generation on Perplexity Computer@AravSrinivas.

AI infrastructure: drug discovery compute scales up, and “Stargate” breaks ground (literally)

Eli Lilly brings a large “AI factory” online

NVIDIA’s blog reported Eli Lilly launched LillyPod, described as the most powerful AI factory wholly owned and operated by a pharmaceutical company: a DGX SuperPOD with 1,016 NVIDIA Blackwell Ultra GPUs, delivering >9,000 petaflops of AI performance (assembled in four months) nvidia. It’s positioned to support large-scale training for protein diffusion, small-molecule graph neural network models, and genomics foundation modelsnvidia, including work like evaluating billions of molecular hypotheses in a computational “dry lab” before committing to wet-lab experiments nvidia.

The post also described Lilly TuneLab, which plans to offer both Lilly models and NVIDIA BioNeMo models using federated learning infrastructure built on NVIDIA FLARE, intended to let biotech companies access proprietary models while keeping their data private/separate nvidia.

OpenAI’s Stargate site begins structural construction

Greg Brockman posted that the first steel beams went up at OpenAI’s Stargate site in Milam County, Texas, with partners SoftBank and SBEnergy tagged in the post @gdb.

Research & developer tooling worth a quick look

Sakana AI: “Doc-to-LoRA” and “Text-to-LoRA” for sub-second customization

Sakana AI Labs introduced Doc-to-LoRA and Text-to-LoRA, using a hypernetwork to generate LoRA adapters on the fly so an LLM can quickly adapt to a task description or internalize a document @SakanaAILabs. They reported sub-second latency@SakanaAILabs and claimed Doc-to-LoRA reached near-perfect accuracy on a needle-in-a-haystack task with inputs 5× longer than the base context window@SakanaAILabs.

Links shared: Doc-to-LoRA paper/code https://arxiv.org/abs/2602.15902 and https://github.com/SakanaAI/Doc-to-LoRA@SakanaAILabs; Text-to-LoRA paper/code https://arxiv.org/abs/2506.06105 and https://github.com/SakanaAI/Text-to-LoRA@SakanaAILabs.

Harbor framework: momentum around shared agent-eval infrastructure

Posts highlighted Harbor as shared infrastructure for agent evaluation, aiming to standardize benchmarks through one interface with repeatable runs and standardized traces@LaudeInstitute. Swyx described Harbor as rapidly dominating RL infra/evals and said their team is prioritizing migrating evals to Harbor @swyx.

Safety & governance: “Department of War” discussion becomes a flashpoint

Anthropic posted a link to a CEO statement by Dario Amodei on the company’s discussions with the Department of War: https://www.anthropic.com/news/statement-department-of-war@AnthropicAI. Gary Marcus called the statement “Historic” and “incredibly brave” @GaryMarcus.

In parallel, Marcus reiterated his view that GenAI is dangerous because it’s unreliable, especially if deployed in high-stakes military contexts without oversight @GaryMarcus.

Big Ideas

1) AI transformation has a predictable “messy middle” — plan for a productivity J-curve, not a straight line

A Product School talk frames AI adoption as a productivity J curve: an initial “fun/pilot” phase, a productivity decline when moving into production (requiring context, data governance, guardrails, systems, and feedback loops), and then a productivity spike that drives business outcomes Product School.

Why it matters: If you treat the dip as failure, teams revert to old workflows before the benefits show up.

How to apply:

1. Treat productionization work (governance + guardrails + feedback loops) as a first-class roadmap item, not “tech debt later” Product School.
2. Tie AI initiatives back to business outcomes (e.g., revenue growth, profit margin, product quality) rather than tracking “AI adoption” as a goal on its own Product School.

2) “AI-native” operating models are forcing org changes across people, tools, and leadership expectations

The same Product School session described a transformation that included:

• People changes: raising the hiring bar to “AI natives,” plus a claim that 1 AI-native person + agents can replace 2 non-AI-native employees without sacrificing productivityProduct School. Leadership was expected to be “builders” / player-coaches (not just managers) Product School.
• Tooling and workflow changes: revamping 66% of SaaS tools, including cutting ~1/3 with no replacement, replacing ~1/3 with AI-native tools, and replacing ~1/3 with products built in-house (“vibe coded”) Product School.

Why it matters: The pattern is less “add AI on top of existing process,” more “use AI to force a workflow rethink.” The tool swap was explicitly described as a way to avoid copy/pasting old processes into new tools Product School.

How to apply:

1. When changing tools, require teams to write down which workflow steps they will delete (not just migrate) Product School.
2. For leadership roles, explicitly assess the “builder” expectation (hands-on ability + judgment + influence + people management) rather than assuming the role is purely oversight Product School.

3) Human-in-the-loop (HITL) should be designed as a strategic allocation of judgment, not a permanent bottleneck

A CNN product/design leader describes a spectrum between control of outcomes and agency, with a general pull toward more agency (scaling quickly, personalization, new user value) but higher risk to user trust and brand integrity because agents are unpredictable Product School. Their core claim: the goal isn’t “zero human involvement,” but to allocate judgment where it counts so you can unlock agency without undermining trustProduct School.

Design framework (what to operationalize):

• When does a human step in? Triggers should be explicit and testable and optimized for product risk (impact if wrong), not the agent’s confidence Product School. Examples include external publication, one-way doors/irreversible actions, blast radius thresholds (users/revenue), regulated/sensitive domains, and scenario-based triggers Product School.
• What does the human do? Choose between binary review (fast/consistent; weak learning signal) and open-ended review (rich learning signal; expensive), often with hybrids/staging over time Product School.
• What happens after the human acts? Capture judgments into prompts/evals/training so HITL reduces future workload; repetition is a signal to encode feedback into the system Product School.
• How does it evolve? Define success criteria and explicit downgrade criteria so HITL doesn’t stick around indefinitely Product School.

Why it matters: Without clear triggers + downgrade criteria, “HITL” becomes an organizational default that slows shipping without improving the system Product School.

4) Product-led growth is shifting toward “agent-led growth” (distribution becomes the constraint)

One Product School framing: speed is no longer the bottleneck and “product is no longer the moat”; building digital products is easier than ever, while growth/distribution is harder Product School. The talk argues that in an “agent-led” world, product teams increasingly optimize for agents discovering the product (e.g., agent marketplaces as new app stores where agents evaluate tools by benchmarks/performance metrics) before humans do Product School.

Why it matters: If discovery and adoption move toward programmatic evaluation, traditional tactics like ads and polished landing pages matter less in some flows Product School.

How to apply:

1. Identify where your product could be evaluated by benchmarks/metrics rather than brand and storytelling, then prioritize what you can measure and improve Product School.
2. Treat “distribution” as an explicit constraint, not an assumption—especially if product build speed increases but adoption doesn’t Product School.

5) Strategy clarity increasingly shows up as storytelling discipline (internally and externally)

Hiten Shah’s framing: “Your story is your strategy, made legible.”@hnshah He argues that if you can’t clearly explain what your company does, you likely don’t have a clear strategy @hnshah. Storytelling forces clarity by revealing gaps when you try to explain the strategy to someone who doesn’t know the space @hnshah. At scale, inconsistent stories across leaders show up as misaligned roadmaps and confused customers @hnshah.

Why it matters: Misalignment isn’t just a communication problem; it becomes a delivery and prioritization problem @hnshah.

How to apply:

1. Write a one-paragraph “what we are / what we aren’t” narrative and pressure-test it with outsiders; use the gaps to surface unresolved strategy decisions @hnshah.
2. Repeat the story until teams can repeat it back (treat it as a scaling mechanism) @hnshah.

Tactical Playbook

1) Build a survey system that acknowledges low response rates (and still produces signal)

A recurring caveat from PMs: general surveys often get 2–5% response rates and skew toward people at the extremes r/ProductManagement. The suggested counter is a portfolio of smaller, better-timed surveys plus analysis discipline.

Tactics you can implement this cycle:

1. Continuous sentiment tracking (quarterly pulse to rotating cohorts): 3 questions (overall satisfaction, top pain point, biggest missing feature) and compare cohort-over-cohort rather than fixating on absolute scores r/ProductManagement.
2. Jobs-to-be-done discovery (1–2×/year): open-ended prompts like “What were you trying to accomplish when you last used [product]?” and “What almost stopped you?” r/ProductManagement.
3. Churn / friction moment surveys: trigger at cancellation, downgrade, or inactivity windows; these can have higher response rates and more actionable data than general satisfaction surveys r/ProductManagement.
4. Feature-specific micro-surveys: after a user completes a key workflow, ask “did this do what you expected?” and “what was confusing?”; use longitudinal comparisons to detect accumulating UX debt r/ProductManagement.

Analysis discipline (to avoid noisy decisions):

• Set a reading quorum: don’t report until you have N responses r/ProductManagement.
• Tag qualitative responses by theme (not just sentiment) r/ProductManagement.
• Cross-reference survey responses with behavioral data; divergence can indicate something important r/ProductManagement.
• Share raw anonymized quotes with engineers/designers (not just summaries) so the team internalizes the user’s language r/ProductManagement.

2) Prevent “late requirement discoveries” without crushing autonomy

A PM leader described a recurring delivery failure mode: late discoveries (edge cases, integration gaps, missing requirements, compliance nuances) that add scope, push timelines, and erode trust with engineering/stakeholders—often because the feature wasn’t fully thought through up front r/ProductManagement.

Coaching structure (simple, repeatable):

1. Past: ask why issues weren’t caught earlier r/ProductManagement.
2. Present: ask what they’re doing now to catch issues sooner r/ProductManagement.
3. Future: require systematic mechanisms (process + documentation like checklists) to catch them earlier next time r/ProductManagement.

Mechanisms that keep rigor high while preserving autonomy:

• Peer review discovery docs/hypotheses to challenge assumptions r/ProductManagement.
• Make risk tolerance explicit: some orgs prioritize value over exhaustive mitigation; others won’t accept the risk. Calibrate expectations accordingly r/ProductManagement +1.
• When perfectionism stalls delivery, explicitly document what’s accepted as a “problem for future us” to help teams descope toward 80–90% value sooner r/ProductManagement.
• For complex domains, consider using AI to generate edge cases from a PRD (as a supplement to human review) r/ProductManagement.

When it becomes performance management: One suggestion was that if the PM isn’t trying to improve (or the above isn’t working), escalation up to a PIP may be warranted r/ProductManagement.

3) Use “HITL triggers + review type” as a product decision (not just a compliance checkbox)

If you’re building agentic workflows, the CNN framework provides practical levers:

1. Define triggers based on risk and blast radius (impact if wrong), not model confidence Product School.
2. Choose the review type:
   • Binary when criteria are clear and you’re gating a specific action (fast/scalable) Product School.
   • Open-ended when nuance/values judgments matter and you want to improve the system over time (expensive but informative) Product School.
3. Treat human feedback as training signal: encode repetitive feedback into prompts/evals so workload declines over time Product School.
4. Define success + downgrade criteria up front so HITL doesn’t become permanent process debt Product School.

4) Interview like a “Context-Builder” (and avoid the two common failure personas)

A recruiter’s breakdown of four PM interview personas argues the one that gets offers is the Context-Builderr/ProductManagement.

The “Context-Builder” pattern (3 beats):

1. Clarify context (“Before I jump in, what I’d want to understand is…”) r/ProductManagement.
2. Give a crisp answer with a real example, decisions, and trade-offs r/ProductManagement.
3. Ask a thoughtful question back that creates collaboration r/ProductManagement.

Two failure modes to watch for:

• Talk-track derailers: don’t answer the question; fix by answering directly in 1–2 sentences, then adding context and asking permission before tangents r/ProductManagement.
• Framework warriors: when frameworks become the star instead of your thinking, founders can feel “handled”; use frameworks mentally as checklists, speak naturally, and adapt to the company stage r/ProductManagement.

Case Studies & Lessons

1) Wise: when opportunity sizing is based on unvalidated behavior assumptions, Excel lies

Wise’s CPO shared a case where 6% of card payments failed due to insufficient balance; the team built an “auto top up” feature and predicted ~10% more card volume, but adoption was closer to 1 in 10,000 users Product School.

Takeaway: “Obvious” value propositions can fail if the user behavior required (setup, trust, habit change) isn’t validated Product School.

2) Wise: small, fast customer testing can unlock outsized growth

A virality PM iterated a transfer-completion email mockup by showing versions to people in a coffee shop until it produced a visceral reaction; by credibly showing “real savings” (€20 vs. €6), referrals increased 300%Product School.

Takeaway: For growth work, the key question may be what customers believe, not what’s technically true in the product UI Product School.

3) Product Hunt launch: ranking creates visibility; onboarding + retention determines whether it matters

A startup’s Product Hunt launch lessons emphasized:

• What helped: clear 1-line positioning, showing real workflows (not feature lists), fast engagement in comments, and cross-platform support to build credibility r/startups.
• What didn’t: traffic didn’t equal retention; many users tested briefly and didn’t explore deeply; without pre-launch audience prep, early momentum was hard; launch-day excitement faded without a follow-up plan r/startups.

A follow-up comment cited outcomes from a separate Product Hunt launch: ~100 accounts directly from Product Hunt, ~300 new accounts after secondary mentions, “several paying customers,” and ongoing 1–3 new accounts/day plus “a customer every 2–3 days” weeks later r/startups.

Takeaway: Treat launch as a top-of-funnel spike; your “real work” is turning first-use into repeat use and retention r/startups.

4) Product School: shipping acceleration came from an “agentic workflow” for course updates

Product School’s CEO said they shipped more product in the last 6 months than the prior two years, launched AI-native courses (AI prototyping, AI evals, advanced agents), and credited a new agentic workflow that reduced course content upgrades from months/quarters to weeksProduct School.

They also described partnerships allowing members to use premium AI tools for free (including OpenAI, Lovable, Linear, Gamma, Replit, Miro, and others), described as worth over $10,000 on the market Product School.

Takeaway: The compounding advantage was not just “building AI content,” but building a workflow that reduced iteration time on the product itself Product School.

Career Corner

1) A PM GitHub is becoming a concrete “builder” signal (and a differentiator)

Aakash Gupta reports that only 24% of PM candidates have a GitHub Product Growth +1, while “every PM” he placed at OpenAI, Anthropic, and Meta AI in the last year did Aakash Gupta. He also reports hiring managers said that if a GitHub is linked, they will check itProduct Growth.

What it signals (per the post):

• You actually build things (not just talk about building) Product Growth.
• You understand how technical teams work Product Growth.
• You can navigate the tools engineers use daily Product Growth.

Example: Shubham Saboo’s GitHub helped him move from DevRel to Senior AI PM at Google Cloud, with the claim that inbound recruiting increases interview-to-offer rate from 22% to 37%Product Growth +1.

2) “PM” titles in big banks can mean P&L + operations management (and may not map cleanly to product building roles)

A thread on Canadian banks described many “PM” roles that are framed as product but cover responsibilities like credit card/portfolio P&L, investment returns, marketing programs, customer acquisition strategy, financial advisor relationships, and call center workforce management r/ProductManagement. One commenter argued “product manager” can be a framework for managing the direction/strategy of a revenue-generating entity r/ProductManagement, while another raised whether this would count as “general PM experience” when applying to roles requiring traditional frontend vs. infra/platform PM work r/ProductManagement.

How to use this: If you’re hiring, tighten your definition of the PM capabilities you need (product discovery/delivery vs. commercial/ops ownership) and screen accordingly r/ProductManagement +1.

3) If you’re not getting offers, shift from “answering” to “shaping the conversation” in interviews

If you tend to be a minimal “Question-Answerer,” the advice is to add (a) business context, (b) one key decision/trade-off, and (c) actual impact r/ProductManagement. If you’re tempted to lead with frameworks, keep them as internal checklists and speak naturally about constraints and first-principles reasoning r/ProductManagement.

Tools & Resources

• Beyond the Pilot: The AI-Native Product Operating Model (Product School, YouTube) — productivity J-curve; builders; shift toward agent-led growth; tool stack revamp Product School.

  • https://www.youtube.com/watch?v=ZuQHYTDDuTU

• Architecting Human-in-the-Loop Agentic Workflows to Scale Judgment (CNN, YouTube) — explicit/testable HITL triggers; binary vs open-ended review; feedback capture; downgrade criteria Product School.

  • https://www.youtube.com/watch?v=StMKWfYOVfE

• Manifesting the Future as a PM: How to Predict & Ship High-Impact Products (Wise, YouTube) — cautionary sizing example + referral lift case study Product School.

  • https://www.youtube.com/watch?v=_e-NwH9Csw4

• This Github Got a PM Hired at Google (Aakash Gupta, Substack) — PM GitHub adoption stats + what hiring managers look for Product Growth +1.

  • https://www.news.aakashg.com/p/you-should-build-a-pm-github

• Community threads worth skimming (for practitioner detail):

  • Survey methodology ideas (PM subreddit) r/ProductManagement
  • Preventing late requirement discoveries (PM subreddit) r/ProductManagement +1
  • Product Hunt launch lessons (r/startups) r/startups +1

Most compelling recommendation: The Hard Thing About Hard Things — operating psychology when the job gets brutal

The Hard Thing About Hard Things (book) — Ben Horowitz

• Content type: Book
• Author/creator: Ben Horowitz
• Link/URL: Not provided in sources (context: recommended in interviews)
• Who recommended it:
  • Jeetu Patel (on Lenny’s Podcast) Lenny's Podcast
  • Brian Halligan (in conversation with Horowitz) Sequoia Capital
• Key takeaway (as shared):
  • Patel highlighted the book as guidance for “how you manage your psychology when things get hard.”Lenny's Podcast
  • Halligan said he read it while running HubSpot, “thought it was really good,” and referenced the book’s stance that it’s “not a fan of the idea of hiring a COO.”Sequoia Capital
• Why it matters: This is a rare “two independent operator endorsements” pattern—and both endorsements point to the same underlying value: decision-making and leadership under stress, not in ideal conditions. Lenny's Podcast +1

Strategy classics that leaders treat as foundational

The Innovator’s Dilemma + The Innovator’s Solution (books) — Clayton Christensen

• Content type: Books
• Author/creator: Clayton Christensen
• Link/URL: Not provided in sources
• Who recommended it: Jeetu Patel Lenny's Podcast
• Key takeaway (as shared): Patel called these books “the Bible in Tech” and said he recommends re-reading them every few years.Lenny's Podcast
• Why it matters: A “re-read every few years” recommendation is a strong signal that the framework keeps paying off as markets, products, and roles change. Lenny's Podcast

Source context: https://www.youtube.com/watch?v=ylNKlBlkFasLenny's Podcast

Investing + career: staying fluid and using surprise as an input

Finite and Infinite Games (book) — James Carse

• Content type: Book
• Author/creator: James Carse
• Link/URL: https://www.generalist.com/p/infinite-gamesThe Generalist
• Who recommended it: The Generalist author (while announcing he’s joining Hummingbird as a partner) The Generalist
• Key takeaway (as shared):

• Why it matters: The author explicitly ties the book to a career philosophy centered on fluidity and “allowing oneself to be surprised,” describing his own best paths as a mix of “shock and inevitability.” The Generalist

Institutions, power, and governance: a regulator-focused reading pick

Reputation and Power (book) — Daniel Carpenter

• Content type: Book
• Author/creator: Daniel Carpenter
• Link/URL: Not provided in sources
• Who recommended it: Balaji Srinivasan On The Brink with Castle Island
• Key takeaway (as shared): Srinivasan called it a “great book” and noted it’s written by someone more sympathetic to the FDA than he is—then added that the FDA has “good aspects” and “bad aspects,” but these aren’t widely discussed, pointing out: “when’s the last time we elected the commissioner of the FDA?” On The Brink with Castle Island
• Why it matters: It’s recommended as a way to reason about regulators via reputation and institutional power, including views that differ from the recommender’s priors. On The Brink with Castle Island

Source context: https://www.youtube.com/watch?v=vGCqe2fMO6sOn The Brink with Castle Island

Political economy + elite psychology (two Balaji picks)

The Sovereign Individual (book)

• Content type: Book
• Author/creator: Not specified in sources
• Link/URL: Not provided in sources
• Who recommended it: Balaji Srinivasan On The Brink with Castle Island
• Key takeaway (as shared): He said he’s “sympathetic” to the sovereign individual thesis and likes the book quite a lot, while also saying he’s increasingly thinking about “the sovereign collective.” On The Brink with Castle Island
• Why it matters: Recommended as a serious framing—even as the recommender signals an active evolution of the thesis toward collective forms. On The Brink with Castle Island

Born Rich (documentary film) — Jamie Johnson

• Content type: Documentary film
• Author/creator: Jamie Johnson
• Link/URL: Not provided in sources
• Who recommended it: Balaji Srinivasan On The Brink with Castle Island
• Key takeaway (as shared): Srinivasan described it as an “interesting movie” about Jamie Johnson (described as an heir to the Johnson & Johnson family) exploring guilt about having lots of money.On The Brink with Castle Island
• Why it matters: A concrete, narrative-format recommendation for understanding how wealth and identity can interact—useful context for founders/investors who operate around extreme outcomes. On The Brink with Castle Island

AI in the real world: an unpolished user interview worth watching

“User Interview #3” (video) — Avi Schiffmann

• Content type: Short video (posted on X)
• Author/creator: Avi Schiffmann
• Link/URL: https://x.com/AviSchiffmann/status/2026798365489725742@garrytan
• Who recommended it: Garry Tan @garrytan
• Key takeaway (as shared):
  • Tan emphasized it’s “not the happy demo path” and “it’s real.” @garrytan
  • The punchline he pulled out: “AI: Doesn’t get tired, doesn’t ghost.”@garrytan
• Why it matters: A strong signal for people tracking AI adoption: the recommendation is explicitly about authentic usage and behavior in practice, not launch-video polish. @garrytan

Resurfaced signal: founder writing clarity as a diligence input

Amazon’s “’97 shareholder letter” (shareholder letter) — Jeff Bezos

• Content type: Shareholder letter
• Author/creator: Jeff Bezos
• Link/URL: Not provided in sources (context: discussed in an X post)
• Who recommended it: Patrick O’Shaughnessy, relaying Dan Sundheim (D1 Capital; “manages over $30B across public and private markets”) @patrick_oshag +1
• Key takeaway (as shared): The post argues that founders who can write clearly are “rare,” and claims that “the only signal that mattered early in Amazon was the ’97 shareholder letter.” @patrick_oshag
• Why it matters: This is another instance of long-form writing being elevated as an early, high-signal artifact when other signals may be noisy. @patrick_oshag

Source post: https://x.com/patrick_oshag/status/2027057100979859525@patrick_oshag

1) Market Movers

Oilseeds: soybeans hit fresh highs, then reversed on China/trade uncertainty

• Feb 26 pricing (international): soybeans down 0.17% to $11.63/bu, while corn and wheat finished higher Canal Rural.
• Farm Journal’s close described soybeans making new highs then “crashing,” tied more to China concerns than month-end profit taking—amid talk that U.S.-China negotiations were “not going well” and with the reminder that Brazilian beans are about $1 cheaperFarm Journal.
• The same segment also flagged that exports were “dismal” and that the U.S. is “too expensive” on the world market, making it hard to sustain upside momentum Farm Journal.

Soybean oil & biofuels: policy optimism vs. heavy inventories

• Farm Journal’s early show emphasized optimism tied to an RVO proposal moving through regulatory channels, while noting crush pace has been below what’s needed for USDA’s forecast and soy oil stocks have climbed for three consecutive monthsFarm Journal.
• Discussion of 45Z included: prioritizing North America (including Canadian canola oil), and removing the indirect land use penalty that had been “penalizing row crop” from a carbon-emissions standpoint—framed as supportive for soybean oil relative to the prior 18 months Farm Journal.

Corn: strong demand narrative, but watch basis and acreage

• Farm Journal’s close framed corn as a split story: cash corn potentially struggling versus Dec futures staying “relatively supported” until acres are secured, with basis a key risk in rallies Farm Journal.
• The same segment cited U.S. demand at 16.47B bu (attributed to USDA), calling it a historically large demand figure Farm Journal.
• Export signal: USDA reported 178,000 MT of corn sold to Japan for delivery in MY 2026/27 (154,000 MT) and MY 2027/28 (24,000 MT)@USDAForeignAg.

Wheat: weather-driven premium fading after a pop

• Farm Journal described wheat consolidating after a “big pop,” with last week’s rally attributed partly to weather premium, but with a more favorable rainfall forecast weighing on follow-through Farm Journal.
• In a separate Farm Journal segment, wheat’s outlook was framed as difficult with positive weather in the forecast for Kansas HRW areas and “the world has plenty of wheat,” implying export-side surprises would matter most Farm Journal.

Livestock: cattle risk headline—JBS strike talk

• Farm Journal’s close flagged strike whispers at the JBS plant in Greeley as a bigger story than month-end profit taking, suggesting it could create sharp downside risk and reinforcing the need to protect cattle price risk near recent highs Farm Journal.

Brazil cash snapshots (Feb 26)

• National physical quotes (selected): soybeans in Rio Grande do Sul at R$122/saca (+R$1), corn in RSR$53/saca (stable), and wheat in ParanáR$1,200/ton (stable) Canal Rural.

2) Innovation Spotlight

Burndown ROI and operational flexibility (U.S.): Reviton programs in no-till

• Product + fit: Reviton (saflufenacil; Group 14 PPO) was positioned as a flexible burndown option in reduced/no-till systems, controlling 50+ broadleaf and grass species, including some glyphosate-, ALS-, and triazine-resistant weeds, and functioning under cool conditions No-Till Farmer.
• Cost math cited in the training: a proactive Reviton program was described as a $5–$10/acre add-on that can avoid rescue passes costing $30–$50/acre, for stated net savings of $15–$40/acre (chemistry + fuel + yield protection) No-Till Farmer.
• Operational note: updated label guidance emphasized rapid plant-back intervals (e.g., 0-day for corn; 0–7 days for soybeans depending on rate/conditions) to “keep the planter moving” No-Till Farmer.

Precision decision support: AI-based fungicide timing

• Pioneer launched an AI-based fungicide timing tool intended to predict disease risk and identify optimal spray windows using field-level data@SuccessfulFarm.

Connected-farm experimentation: Starlink for irrigation + sensing

• Nick Horob described plans to irrigate trees/small plots on an arid 500-acre farm using a productive well and Starlink-connected pumps and switches for remote operation @NickHorob.
• He also plans to extend Starlink across the farm to support soil moisture sensors, cameras, and other experiments, with the intention to share results and possibly live data feeds @NickHorob.

Post-harvest automation: bin monitoring to protect grain quality

• An Indiana farmer highlighted an AGI 137,000-bushel bin and a bin manager system that can automatically run fans to manage moisture/temperature risk, framed as protecting $600k–$700k of grain value in a single bin and reducing “risk factor” and quality anxiety Brownfield Ag News.

On-farm feed manufacturing: grinder mixers to reduce custom costs

• Art’s-Way’s largest grinder mixer (model 8215) was described as a 215-bushel tank, 26-inch hammer mill, roughly 6-ton capacity unit (diet-dependent), requiring 90–100 PTO HPFarm4Profit Podcast.
• Cost lever cited: avoiding $10–$16/ton custom grind/mix delivery by using more grain on-farm with less handling/transport Farm4Profit Podcast.

Small-/mid-scale tech adoption (Brazil): drones + automation with payback windows

• A Canal Rural segment said replacing conventional sprayers with drones can eliminate trampling (“amassamento”) losses and recover productivity in tramlines from the first harvestCanal Rural.
• It also pointed to automation options that can pencil for smaller farms (e.g., pumping/irrigation and water supply for small animals) Canal Rural and noted that automated feeding systems for small animals can show better economic consistency after 4–5 cycles, assuming management adjustments accompany the tech Canal Rural.

3) Regional Developments

Brazil (Mato Grosso + Northern Arc): rain + logistics disrupting soybean harvest execution

• Field conditions: reports cited 2,300 mm of accumulated rain versus an 1,800 mm annual average, with harvesters getting stuck and conditions also complicating corn planting Canal Rural.
• Crop progress and losses: Mato Grosso soybean harvest was cited at 66% complete, with corn planting at 65% by Feb 20, both slowed by sustained rainfall; estimated soybean losses were discussed at ~15% in affected areas Canal Rural.
• Export bottleneck: the road access to Miritituba (Pará) port terminals was linked to truck queues up to 30 km, driven by poor road conditions (worse when it rains), leaving terminals short of trucks and slowing discharge despite daily targets Canal Rural.

• Economic impact signals: producers described losing 10–15 BRL per soy sack due to logistics delays Canal Rural. Another report highlighted BR-163 challenges (including a toll cited at R$676.80) alongside potholes/accident risk, framed as compounding transport difficulty during peak harvest Canal Rural.

Mercosur–EU agreement: ratification pace hinges on safeguards

• Status: the agreement was approved in the lower houses of Brazil and Argentina, while Brazil’s Senate was described as awaiting a government safeguards decree before taking the text to a plenary vote Canal Rural.
• Safeguards definition in the reporting: tools include import quotas, suspending tariff reductions, and restoring prior tax levelsCanal Rural.

Brazil rice: commercialization support tied to falling prices

• Conab announced releasing R$73M+ to support rice commercialization for the 2025/26 crop, with an estimate that it could support around 300,000 tons depending on premium size; R$61.3M was allocated to Rio Grande do Sul, linked to moving about 250,000 tonsCanal Rural.

Animal health trade impact: Argentina avian influenza

• Argentina’s SENASA confirmed a highly pathogenic avian influenza case in Buenos Aires province, triggering quarantine/disinfection/culling and a suspension of poultry product exports to trading partners Canal Rural.

U.S. conditions watch: moisture + animal health

• Iowa drought: moderate drought was reported to jump from 11% to 25% in a week (February U.S. Drought Monitor framing) as planting season approaches @SuccessfulFarm.
• Iowa poultry: the Iowa Department of Agriculture and Land Stewardship reported two HPAI cases in flocks in Keokuk and Van Buren counties @SuccessfulFarm.

Soft commodities: Ghana cocoa financing stress

• A Reddit post flagged reports that Ghana cocoa buyers may owe banks up to $750M, framed as financing stress that could tighten forward selling/hedging capacity and exporter liquidity, with potential price impacts showing up with a lag r/AgriTech.

4) Best Practices

Weed control in tight spring windows (no-till burndown)

• Timing priority under cold/wet conditions: in a Q&A, the guidance was to prioritize weed size and forecast (treated as “one and one”) ahead of other factors when windows are tight No-Till Farmer.
• Species-specific timing examples: ryegrass control was framed as best at the 1–2 leaf stage, while henbit can be controlled later (up to bloom) No-Till Farmer.
• Spray setup details cited: increase carrier volume to 15–20 GPA, use a full MSO load (1% v/v), and avoid large-droplet nozzles—using smaller droplet sizing such as a flat-fan nozzle in residue/cool conditions No-Till Farmer.

Balancing burndown timing with soil biology (cover crops)

• A no-till training emphasized that burn down timing “really does matter” because when living roots disappear, microbial activity can drop quickly; later termination was framed as supporting nutrient cycling closer to crop demand and reducing erosion risk on highly erodible soils No-Till Farmer.

Winter grazing systems: stockpiling and targeted hay “unrolling”

• A Missouri grazing strategy described using “one bite and leave” through the season to thicken the grass sward and stockpile the entire farm by fall Practical Farmers of Iowa.
• Winter management included rotating twice daily on stockpiled forage (rain-dependent), and using purchased hay placed strategically so not all of it is eaten—leaving some residue for soil microbes Practical Farmers of Iowa.

Pastured poultry: reducing predation pressure with layered defenses

• Practical tactics cited included guard animals (including aggressive geese) Douglas Holloway, moving egg mobiles/netting to disrupt predator routines Douglas Holloway, and electric netting paired with short-grass “DMZ” zones to deter smaller predators Douglas Holloway.

5) Input Markets

Fertility cost pressure (Brazil): rising fertilizer costs vs. weaker soybean prices

• In Mato Grosso, one segment described fertilizer costs (N/P/K) running ~25% higher versus the prior season, while soybean prices were described around ~R$100/saca, down from R$170–R$180 during the pandemic period Canal Rural.

Biofuel-driven demand debates: soybean oil needs “clear” demand signals

• One Farm Journal segment said crush pace has been below the pace needed for USDA’s forecast while soy oil stocks climbed for three straight months, arguing the market needs positive policy/demand news that prioritizes domestic feedstocks to help draw down inventories Farm Journal.

Crop protection and trait positioning

• Syngenta’s DuraStack trait technology was promoted as featuring three modes of action and a triple Bt protein stack for corn rootworm control, with rootworm costs cited at up to $1B/year and availability framed for the 2027 seasonFarm Journal.

Feed manufacturing + quality variability

• A sugar beet processing challenge was described: some areas saw very high tonnage (30–40 tons/acre) but low sugar (15–16%) versus a traditional 20% sugar target, which was described as inefficient for co-ops processing beets Farm4Profit Podcast.

6) Forward Outlook

Weather windows (Brazil): near-term fieldwork vs. incoming rains

• Canal Rural weather coverage described the Centro-Sul as currently hot/dry (especially Mato Grosso do Sul and interior São Paulo), with more substantial rains expected from next weekend and stronger volumes in the 9–13 March window for areas including Paraná, interior SP, and MS Canal Rural.
• The same coverage called out >100 mm rains forecast for parts of Brazil’s interior Northeast (BA/MA/PI/PE interior), framed as helpful for reversing water deficits Canal Rural.

2026 acreage direction (U.S.): “soybean swing” vs. corn pullback

• A Farm Journal discussion suggested that a 94M corn-acre number would be viewed as “very friendly,” but argued corn acres likely back off from outlier years (with crop insurance economics still a factor) Farm Journal.
• Another Farm Journal source forecasted soybean acres at 86M (vs. USDA’s 85M and 81.2M last year) and corn acres at 94M, tied to price ratios, renewable fuel buildout expectations, and farmer-held unsold corn stocks Farm Journal.

Logistics as a continuing price/spread variable (Brazil)

• In Mato Grosso’s northern route, one report warned harvest is still early—“not 5% of areas” have moved through the route yet—implying congestion risk remains as volumes build Canal Rural.

Items to monitor

• China-driven soybean volatility: the same factors that drove Feb 26’s reversal (China concern + U.S. price competitiveness) remain a headline risk Farm Journal.
• Livestock disruption risk: strike talk at major packing plants was framed as a sudden downside catalyst for cattle markets Farm Journal.

🔥 TOP SIGNAL

Orchestration is becoming the core dev skill: Addy Osmani argues the enterprise frontier is orchestrating a modest set of agents with control/traceability, not running huge swarms O'Reilly. In practice, that shows up as spec-first work: Brendan Long’s vibe-coding loop starts by writing a detailed GitHub issue ("90% of the work"), optionally having an agent plan, then having another agent implement brendanlong.

🛠️ TOOLS & MODELS

• Codex CLI v0.105 (major QoL upgrade)@thsottiaux

  • New: syntax highlighting, dictate prompts by holding spacebar, better multi-agent workflows, improved approval controls, plus other QoL changes @thsottiaux.
  • Install/upgrade: $ npm i -g @openai/codex@latest@thsottiaux.
  • Practitioner reaction: “diffs are beautiful” and it’s “very, very fast now” @iannuttall.

• Codex app (Windows) — first waitlist batch invited@thsottiaux

  • Team says they’ll “expand from there” as they iterate through feedback @thsottiaux.

• Model preference + benchmarkging signals (Codex 5.3)

  • Mitchell Hashimoto: Codex 5.3 felt “much more effective” than Opus 4.6; after switching back-and-forth, he hasn’t touched Opus for a week @mitchellh.
  • Romain Huet: GPT-5.3-Codex hit 90% on IBench at xhigh reasoning; says with speed gains, “xhigh doesn’t feel like a tradeoff anymore” @romainhuet.
  • Related run: “decided to run 5.3 codex on xhigh as well, its 90%… rip IBench, survived 3 months” @adonis_singh.

• Cursor — Bugbot Autofix (PR issues → auto-fixes)@cursor_ai

  • Announcement: Bugbot can now automatically fix issues it finds in PRs @cursor_ai.
  • Details: http://cursor.com/blog/bugbot-autofix@cursor_ai.

• Devin AI (real production debugging)

  • swyx reports Devin investigated a production bug (Vercel org migration + forgotten key), asked for exactly what it needed, and verified the fix @swyx.

• FactoryAI Droids — “Missions” + terminal “Mission Control”

  • “Missions”: multi-day autonomous goals where you describe what you want, approve a plan, and come back to finished work @FactoryAI +1.
  • Mission Control: a terminal view of which feature is being built, which Droid is on it, tools used, and progress @FactoryAI.
  • Examples FactoryAI says enterprises are running: modernize a 40-year-old COBOL module; migrate >1k microservices across regions; recalc 10 years of pricing; refactor a monolith handling 20M daily API calls with no downtime @matanSF.

• OpenClaw — new beta bits@steipete

  • Adds: external secrets management (openclaw secrets) @steipete +1, CP thread-bound agents @steipete, WebSocket support for Codex @steipete, and Codex/Claude Code as first-class subagents via ACP @steipete.

• Omarchy 3.4 — agent features shipped

  • Release highlights include “new agent features (claude by default + tmux swarm!)” and a tailored tmux setup @dhh +1.

• Harbor framework — shared agent eval infra momentum

  • Laude Institute frames Harbor as shared infrastructure to standardize benchmarks via one interface (repeatable runs, standardized traces, production-grade practice) @LaudeInstitute.
  • swyx says his team is prioritizing migrating evals to Harbor and calls it dominant in RL infra/evals for terminal agents @swyx.

💡 WORKFLOWS & TRICKS

• Spec-driven agent work (make the spec the artifact)

  • Brendan Long’s repeatable loop for large vibe-coded apps:
    1. Write a GitHub issue brendanlong
    2. If it’s complex, have an agent produce a plan and update the issue brendanlong
    3. Have another agent read the issue and implement it brendanlong
  • He claims a detailed enough issue is “90% of the work” and rewriting it is often what fixes problems brendanlong.

• Enterprise-grade orchestration guidance (modest fleets, strong controls)

  • Addy Osmani’s concrete advice: spend 30–40% of task time writing the spec—constraints, success criteria, stack/architecture—and gather context in a resources directory; otherwise you “waste tokens” and LLMs default to “lowest common denominator” patterns O'Reilly.
  • For teams: codify best practices in context (e.g., MCP-callable systems or even markdown files) to raise the odds the output is shippable O'Reilly.
  • He also flags the real bottleneck: “Not generation, but coordination” O'Reilly.

• Close the loop: isolate the runtime so agents can run it

  • Kent C. Dodds: “get your app running in an isolated environment to close the agent loop” @kentcdodds.
  • He points to his Epic Stack guiding principles—“Minimize Setup Friction” and “Offline Development”—as a practical way to make this easier @kentcdodds.

• Hoard working examples, then recombine (prompt with concrete known-good snippets)

  • Simon Willison’s pattern: keep a personal library of solved examples across blogs/TIL, many repos, and small “HTML tools” pages, because agents can recombine them quickly simonwillison.
  • His OCR tool story: he combined snippets for PDF rendering and OCR into a single HTML page via prompt, iterated a few times, and ended with a tool he still uses simonwillison.
  • Agent tip: when asking Claude Code to reuse an existing tool, he sometimes specifies curl explicitly to fetch raw HTML instead of a summarizing fetch tool simonwillison.

• Tests aren’t a moat anymore (agents can recreate them fast)

  • tldraw moved tests to a closed-source repo to prevent “Slop Fork” forks @cramforce.
  • Armin Ronacher’s counterpoint: agents can generate language/implementation-agnostic test suites quickly if there’s a reference implementation @mitsuhiko.

• Security footnote from a vibe-coded app

  • In Simon Willison’s Present.app walkthrough, the remote-control web server used GET requests for state changes (e.g., /next, /prev), which he notes opens up CSRF vulnerabilities—he didn’t care for that application Simon Willison’s Newsletter.

👤 PEOPLE TO WATCH

• Addy Osmani (Google Cloud AI) — clearest “enterprise reality check”: quality bars, traceability, and spec/context discipline, plus a strong stance that orchestration is the thing to learn O'Reilly.
• Simon Willison — consistently turns agent usage into transferable patterns (Agentic Engineering Patterns + “hoard examples” + codebase walkthrough prompts) Simon Willison’s Newsletter +1.
• Brendan Long — practical decomposition: write issues like a system design interview, then let agents execute brendanlong.
• Nicholas Moy (DeepMind) — framing: “10x engineer” becomes “10 agent orchestrator,” measured by concurrent agents you can run effectively @thenickmoy.
• Dylan Patel (Semianalysis) — adoption signal: Claude Code share of GitHub commits going 2%→4% in a month, with a broader estimate of total AI-written code around ~10% Latent Space.

🎬 WATCH & LISTEN

1) Addy Osmani: “Learn orchestration” + the path to agent fleets (≈ 23:32–25:54)

Hook: Practical roadmap from single-agent prompting to multi-agent orchestration and coordination patterns—before you burn tokens on experimental swarms O'Reilly.

2) SAIL LIVE #6: why SWE-Bench got saturated (and what that says about evals) (≈ 29:49–33:48)

Hook: A clear explanation of how SWE-Bench is constructed, why it became the default “agentic coding” benchmark, and why that creates problems once it’s widely known and reused SAIL Media.

📊 PROJECTS & REPOS

• Agentic Engineering Patterns (Simon Willison) — a living guide of coding-agent practices and patterns (agentic engineering vs vibe coding framing) Simon Willison’s Newsletter

  • https://simonwillison.net/guides/agentic-engineering-patterns/Simon Willison’s Newsletter

• Present.app (Simon Willison) — vibe-coded SwiftUI macOS presentation app where each “slide” is a URL; GitHub repo shared Simon Willison’s Newsletter

  • https://github.com/simonw/presentSimon Willison’s Newsletter

• OpenClaw releases + docs (beta features shipping)@steipete

  • https://github.com/openclaw/openclaw/releases@steipete
  • Secrets docs: https://docs.openclaw.ai/cli/secrets@steipete
  • ACP agents docs: https://docs.openclaw.ai/tools/acp-agents@steipete

• Cursor Bugbot Autofix announcement + writeup@cursor_ai

  • http://cursor.com/blog/bugbot-autofix@cursor_ai

• Omarchy 3.4 release (61 contributors; agent features + tmux work) @dhh

  • https://github.com/basecamp/omarchy/releases/tag/v3.4.0@dhh

• tldraw tests move discussion (tests closed-source) @cramforce

  • https://github.com/tldraw/tldraw/issues/8082@cramforce

Editorial take: The leverage is shifting from “pick the best model” to “build the tightest loop”: spec → isolated runtime → tests/evals → approvals—and only then scale agents.

Top Stories

1) Google’s Nano Banana 2 takes #1 in major image leaderboards and rolls out broadly

Why it matters: Image generation is becoming a default capability inside mainstream consumer and developer surfaces; the differentiators are now cost, text rendering, and grounding to real-world conditions via search.

Google DeepMind launched Nano Banana 2 (officially Gemini 3.1 Flash Image Preview) as a state-of-the-art image generation and editing model built on the latest Gemini Flash, aiming for Pro-level capabilities at “Flash” speed@GoogleDeepMind +1. In LMSYS Image Arena, it debuted at #1 and is described as powered by real-time web search information and images@arena.

Key benchmark and pricing callouts:

• Image Arena: #1 Text-to-Image (1279) and ties #1 Single-Image Edit (1407); Top 3 Multi-Image Edit@arena
• Price: $0.067 per image (about 2× cheaper than Nano Banana Pro) @arena
• Text rendering: Image Arena highlighted a 60+ point lead over Nano Banana Pro in text rendering @arena

Google also emphasized the model’s production features (e.g., 512px → 4K upscaling, aspect ratio control, and consistency up to 5 characters + 14 objects) @Google. Rollout includes Gemini App, Search (141 countries), Flow, and preview via AI Studio and Vertex AI @sundarpichai.

2) Anthropic draws explicit red lines in a dispute with the U.S. Department of War

Why it matters: The frontier-lab/government relationship is shifting from abstract “AI policy” debates to contractual demands and enforcement mechanisms.

Anthropic says the Department of War will only contract with AI companies that accede to “any lawful use” and remove safeguards, and that it has threatened to remove Anthropic from military systems, designate it a “supply chain risk,” and invoke the Defense Production Act to force safeguards’ removal @AndrewCurran_.

In the statement, Anthropic describes two excluded use cases:

• Mass domestic surveillance, which it says is “incompatible with democratic values,” and that powerful AI can assemble scattered data into “a comprehensive picture of any person’s life—automatically and at massive scale” @AndrewCurran_.
• Fully autonomous weapons without oversight, arguing today’s frontier AI systems are “not reliable enough” and that proper guardrails “don’t exist today” @AndrewCurran_.

A separate report summarized the stance as refusing to build tools for mass surveillance of U.S. citizens or autonomous weapons without human oversight, despite pressure tied to system access and “supply chain risk” threats @TheRundownAI.

3) Perplexity expands from “search app” to platform integration: Samsung Galaxy S26 + new embedding models

Why it matters: Distribution is moving “down the stack” into OS-level assistants, while retrieval quality becomes a product-level moat.

Perplexity announced it will be built into upcoming Samsung Galaxy S26 devices as a system-level AI with a dedicated wake word (“Hey Plex”), positioned as the first time Samsung has granted OS-level access to a non-Samsung/Google app @perplexity_ai +1. Perplexity says S26 users can launch it via wake word or side button, and that system-level integration enables it to read from and write to Samsung’s core apps directly@AravSrinivas.

Samsung’s Bixby is described as routing complex, web-based, or generative queries to Perplexity APIs, combining real-time web search with LLM reasoning @perplexity_ai +1.

Perplexity also released two embedding model families—pplx-embed-v1 and pplx-embed-context-v1—described as designed for real-world, web-scale retrieval@perplexity_ai. In a separate thread, Perplexity claimed internal web-scale benchmarks with 100K+ real user queries over 1B+ web pages showed strong performance versus competitors @AravSrinivas. Links: https://pplx.ai/pplx-embed@AravSrinivas and quickstart docs https://docs.perplexity.ai/docs/embeddings/quickstart@AravSrinivas.

4) ETH Zurich + Anthropic research highlights low-cost, automated deanonymization at scale

Why it matters: As reasoning and retrieval improve, “anonymous posting” can become fragile—even when no single step looks like deanonymization.

A paper described as “Large-Scale Online Deanonymization with LLMs” demonstrates an automated ESRC pipeline (Extract identity signals → Search via embeddings → Reason → Calibrate), reported to work on platforms including Hacker News and Reddit, with claims of roughly $1 per target @alex_prompter +1. Reported results included 67% correct identification on Hacker News users with 90% precision when guessing@alex_prompter, plus performance on pseudonymous Reddit academics and redacted interviews @alex_prompter.

5) AI-driven capex continues scaling: hyperscaler spending nears half a trillion (2025) with larger projections ahead

Why it matters: Many “model progress” stories are increasingly constrained or enabled by capital deployment (compute, energy, and data center buildout).

Epoch AI Research reported hyperscaler capex driven by AI has grown ~70% per year since GPT-4, nearing $500B total in 2025@EpochAIResearch. If the trend continues, Alphabet, Amazon, Meta, Microsoft, and Oracle could spend $770B on capex in 2026 @EpochAIResearch. Another projection cited ~$800B in 2026 and >$1T per year in 2027 @kimmonismus. Epoch also noted it used a consistent capex measure from financial filings because companies define “capex” differently on earnings calls @EpochAIResearch.

Research & Innovation

Infrastructure + training efficiency: new techniques target bottlenecks across datacenters and inference systems

Why it matters: Many of the biggest capability jumps are increasingly gated by systems-level throughput and training logistics, not only model architecture.

• MuLoCo: A pre-training optimizer positioned as enabling efficient frontier LLM pre-training across datacenters with large enough batch sizes; extends Muon’s advantages to distributed, quantized, and large-scale training, with released code @benjamintherien +1.
• DeepSeek DualPath: Proposes that KV-cache loading “does not have to be prefill-centric,” introducing a storage-to-decode path where KV-cache is loaded into a decode engine first, then transferred to prefill via high-bandwidth RDMA; claims up to 1.87× offline inference throughput and 1.96× higher online agent runs/sec @teortaxesTex +1.

Multi-agent coding efficiency: dynamic topology beats static workflows

Why it matters: As coding agents become standard, orchestration choices can materially change both accuracy and cost.

AgentConductor uses an RL “orchestrator agent” to dynamically generate task-adapted interaction topologies based on inferred agent roles and difficulty @dair_ai. Across five code datasets, it reported up to 14.6% pass@1 improvement, 13% density reduction, and 68% token cost savings @dair_ai. Paper: https://arxiv.org/abs/2602.17100@dair_ai.

World models and shared state: “multiplayer” world modeling in Minecraft

Why it matters: If world models move from “one agent’s view” to persistent shared world state, they can become a substrate for multi-agent coordination.

Project Solaris introduced a multiplayer video world model effort in Minecraft, arguing that “world state is global” and that shared representations beneath individual views are what scale into collective capability @sainingxie. It includes a multiplayer data collection engine, a multiplayer DiT model trained on 12.6M frames of coordinated gameplay, and an evaluation approach using a VLM-as-judge @sainingxie. Project site: https://solaris-wm.github.io/@sainingxie.

Fast, on-the-fly model customization: Doc-to-LoRA and Text-to-LoRA

Why it matters: Techniques that compress “customization” into a fast forward pass can reduce reliance on long prompts or expensive fine-tuning.

Sakana AI Labs introduced Doc-to-LoRA and Text-to-LoRA, using a hypernetwork to generate LoRA adapters on demand to internalize new information or adapt to tasks @SakanaAILabs. They report sub-second latency and released code/papers @SakanaAILabs. Doc-to-LoRA paper: https://arxiv.org/abs/2602.15902@SakanaAILabs; Text-to-LoRA paper: https://arxiv.org/abs/2506.06105@SakanaAILabs.

Interpretability for video world models: V-JEPA 2

Why it matters: Understanding representations used by video models can clarify what they’re actually learning (and what “physics simulation” claims do or don’t mean).

Meta released Interpreting Physics in Video World Models, described as one of the first interpretability studies of video encoders @soniajoseph_. The work suggests modern video models use distributed representations (not factorized variables like a classical physics engine) that are nevertheless sufficient for making physical predictions @raphaelmilliere.

Products & Launches

“Do the work” interfaces: agents that execute multi-step tasks (not just chat)

Why it matters: The UI shift is toward delegation with oversight—describe a goal, review a plan, get finished output.

• Microsoft Copilot Tasks (Research Preview): Positioned as AI that “talks less and does more,” enabling users to delegate work in plain language, review the plan, and stay in control @yusuf_i_mehdi. Example tasks include turning a syllabus into a study plan, tracking apartment listings and booking showings, and triaging emails with draft replies + auto-unsubscribe @mustafasuleyman. Waitlist: https://copilot.microsoft.com/tasks/preview?form=M301EQ&OCID=CGE_osocial_Copilot_Free_868hmzrvb@mustafasuleyman.
• FactoryAI “Missions” for Droids: Described as goal-oriented, multi-day autonomous work—describe what you want, approve the plan, and come back to finished work @FactoryAI. Examples included modernizing a 40-year-old COBOL module and migrating >1k microservices across regions @matanSF.

Developer tooling updates: coding agents and IDE assistance

Why it matters: As more code is generated by agents, teams need better review, debugging, and safe execution environments.

• Claude Code auto-memory: Reported as a new auto-memory feature where Claude remembers project context and preferred approaches across sessions @trq212 +1.
• Cursor Bugbot Autofix: Automatically fixes issues it finds in PRs @cursor_ai. Details: http://cursor.com/blog/bugbot-autofix@cursor_ai.
• VS Code long-distance NES: Next Edit Suggestions can now propose edits anywhere in a file, not only near the cursor @code +1.
• Ollama “Pi”: A minimal coding agent launchable via ollama launch pi, described as customizable and able to write extensions for itself @ollama. Docs: https://docs.ollama.com/integrations/pi@ollama.

New creative and multimodal models shipping to users

Why it matters: Model quality matters, but availability through APIs and platforms determines real adoption.

• PrunaAI P-Video: Launched with claims of fast/cheap video generation (e.g., 10s to generate 5s at 720p; $0.02/s at 720p and $0.04/s at 1080p) @PrunaAI. It entered Video Arena (e.g., #22 Text-to-Video score 1178) @arena.
• Kling 3.0: Released as an all-in-one multimodal creative engine; Kling V3 Pro reached Video Arena top 10 for Image-to-Video (tied #8, score 1337) @Kling_ai +1.
• QuiverAI Arrow 1.0: Public beta for an SVG AI model (“turn your ideas into graphics”) @QuiverAI.

Industry Moves

Capital, partnerships, and major organizational shifts

Why it matters: Distribution, compute access, and enterprise channels increasingly determine who can scale.

• Amazon–OpenAI investment talks: A report claimed Amazon is negotiating a potential $50B investment in OpenAI—$15B upfront plus $35B tied to either an IPO or achieving AGI @kimmonismus +1.
• Sakana AI × Datadog: Strategic partnership focused on enterprise AI innovation and observability, with joint research, potential open-source contributions, and go-to-market efforts @SakanaAILabs. Press release: https://www.datadoghq.com/about/latest-news/press-releases/datadog-sakana-ai-strategic-partnership/@SakanaAILabs.
• Block workforce reduction: Jack Dorsey said Block is reducing headcount from over 10,000 to under 6,000, citing “intelligence tools” enabling smaller, flatter teams and a fundamentally new way of working @jack.
• Thinking Machines Lab departures: Two more founding members of Mira Murati’s Thinking Machines Lab reportedly left for Meta; the startup raised $2B last year @CharlesRollet1.

Policy & Regulation

Defense contracting pressure + “supply chain risk” as a leverage point

Why it matters: “Supply chain risk” designations and Defense Production Act threats represent high-leverage tools that can reshape AI vendor behavior.

Anthropic describes threats including a “supply chain risk” label and potential Defense Production Act invocation to compel safeguard removal @AndrewCurran_. Reporting also described a “best and final” offer for unrestricted military use and possible contract penalties if refused @DeItaone.

A separate thread argued the Department of War has latitude to scope “supply chain risk” narrowly (limiting Claude in sensitive systems without broader business harm) or broadly (potentially forcing contractors to cut ties and raising regulatory risk premia across AI) @deanwball.

Privacy risk from LLM-enabled deanonymization

Why it matters: As ESRC-style pipelines become cheaper, “anonymous” posts may become linkable at scale.

The ETH Zurich + Anthropic paper summary emphasizes that more reasoning compute can improve deanonymization and that the pipeline can be hard to block because it decomposes into benign-looking subtasks (summarize profile, compute embeddings, rank candidates) @alex_prompter.

Quick Takes

Why it matters: Smaller updates often reveal where the ecosystem is hardening—benchmarks, eval tooling, and deployment surfaces.

• Claude Opus 4.6 reached #1 across Text, Code, and Search Arena, with Search Arena score 1255 (+30 over Grok-4.20-beta1, GPT-5.2, and Gemini-3) @arena.
• Code Arena launched a Multi-File React leaderboard to evaluate cross-file coordination, dependency management, state management, and build reliability @arena.
• SWE-bench Multilingual: reported scores slowly moved from ~65% toward ~75%, while average cost-to-solve dropped from $0.67 to $0.10 with Minimax 2.5 @OfirPress.
• Qwen3.5-27B was reported at nearly 50% on Humanity’s Last Exam (HLE) @kimmonismus.
• vLLM 0.16.0 released: https://github.com/vllm-project/vllm/releases/tag/v0.16.0@vllm_project.

What mattered today

Perplexity lands OS-level distribution on Samsung Galaxy S26

Perplexity CEO Aravind Srinivas said Perplexity will be integrated into all Samsung Galaxy S26 phones, preloaded with the wake word “Hey Plex”, alongside Bixby and Gemini @AravSrinivas. He also said Bixby will use Perplexity’s search-grounded LLMs/APIs on the backend for search and reasoning, aiming for “grounded, up-to-date answers” via real-time web search + LLM reasoning @AravSrinivas.

Srinivas described this as system-level integration (wake word, access to physical controls) with the ability to read from and write to native apps like Notes, Calendar, Gallery, Clock, and Reminders @AravSrinivas +1. He also claimed Perplexity is the first non-Google company to have OS-level access in a Samsung phone, and that Samsung is expected to ship hundreds of millions of devices this year with Perplexity powering assistants, browser agents, and search @AravSrinivas +1.

More details were linked here: https://www.perplexity.ai/hub/blog/perplexity-apis-deliver-powerful-ai-to-the-world%E2%80%99s-largest-android-device-maker@AravSrinivas.

Google ships “Nano Banana 2” (Gemini 3.1 Flash Image Preview) with web-search grounding

Multiple Google/DeepMind leaders highlighted the release of Nano Banana 2, officially Gemini 3.1 Flash Image Preview, which is described as being powered by real-time information and images from web search@arena. On Image Arena, it debuted at #1 in Text-to-Image (1279) and tied #1 in Single-Image Edit (1407), with top-3 Multi-Image Edit and a listed cost of $0.067 per image (~2× cheaper than Nano Banana Pro) @arena.

Google also positioned it as rolling out broadly: default in the Gemini app, Search (141 countries), and Flow, plus preview access via AI Studio and Vertex AI@sundarpichai. Sundar Pichai pointed to a “Window Seat” demo that generates window views worldwide using live local weather in 2K/4K specs@sundarpichai.

Agents: more “set a goal, come back later” products

Microsoft: “Copilot Tasks” enters research preview

Microsoft launched Copilot Tasks, positioning it as “AI that talks less and does more,” with no complicated setup or coding—users ask for what they need and Copilot handles the rest @mustafasuleyman. Examples included turning a syllabus into a study plan (with practice tests and blocked focus time), tracking apartment listings and booking showings, and triaging email with drafted replies and auto-unsubscribe actions @mustafasuleyman.

It’s in a Research Preview for a small group, with a waitlist here: https://copilot.microsoft.com/tasks/preview?form=M301EQ&OCID=CGE_osocial_Copilot_Free_868hmzrvb@mustafasuleyman.

FactoryAI: “Missions” for multi-day autonomous work

FactoryAI announced “Missions” for its “Droids,” describing goal-oriented work that can run over multi-day horizons: you describe the goal, approve the plan, and return to finished work @FactoryAI +1. Example targets included large enterprise changes like migrating >1k microservices across regions or refactoring a monolith handling 20M daily API calls without downtime @matanSF.

Perplexity: Nano Banana 2 available inside Perplexity Computer

Separately, Srinivas said Nano Banana 2 is now available for image generation on Perplexity Computer@AravSrinivas.

AI infrastructure: drug discovery compute scales up, and “Stargate” breaks ground (literally)

Eli Lilly brings a large “AI factory” online

NVIDIA’s blog reported Eli Lilly launched LillyPod, described as the most powerful AI factory wholly owned and operated by a pharmaceutical company: a DGX SuperPOD with 1,016 NVIDIA Blackwell Ultra GPUs, delivering >9,000 petaflops of AI performance (assembled in four months) nvidia. It’s positioned to support large-scale training for protein diffusion, small-molecule graph neural network models, and genomics foundation modelsnvidia, including work like evaluating billions of molecular hypotheses in a computational “dry lab” before committing to wet-lab experiments nvidia.

The post also described Lilly TuneLab, which plans to offer both Lilly models and NVIDIA BioNeMo models using federated learning infrastructure built on NVIDIA FLARE, intended to let biotech companies access proprietary models while keeping their data private/separate nvidia.

OpenAI’s Stargate site begins structural construction

Greg Brockman posted that the first steel beams went up at OpenAI’s Stargate site in Milam County, Texas, with partners SoftBank and SBEnergy tagged in the post @gdb.

Research & developer tooling worth a quick look

Sakana AI: “Doc-to-LoRA” and “Text-to-LoRA” for sub-second customization

Sakana AI Labs introduced Doc-to-LoRA and Text-to-LoRA, using a hypernetwork to generate LoRA adapters on the fly so an LLM can quickly adapt to a task description or internalize a document @SakanaAILabs. They reported sub-second latency@SakanaAILabs and claimed Doc-to-LoRA reached near-perfect accuracy on a needle-in-a-haystack task with inputs 5× longer than the base context window@SakanaAILabs.

Links shared: Doc-to-LoRA paper/code https://arxiv.org/abs/2602.15902 and https://github.com/SakanaAI/Doc-to-LoRA@SakanaAILabs; Text-to-LoRA paper/code https://arxiv.org/abs/2506.06105 and https://github.com/SakanaAI/Text-to-LoRA@SakanaAILabs.

Harbor framework: momentum around shared agent-eval infrastructure

Posts highlighted Harbor as shared infrastructure for agent evaluation, aiming to standardize benchmarks through one interface with repeatable runs and standardized traces@LaudeInstitute. Swyx described Harbor as rapidly dominating RL infra/evals and said their team is prioritizing migrating evals to Harbor @swyx.

Safety & governance: “Department of War” discussion becomes a flashpoint

Anthropic posted a link to a CEO statement by Dario Amodei on the company’s discussions with the Department of War: https://www.anthropic.com/news/statement-department-of-war@AnthropicAI. Gary Marcus called the statement “Historic” and “incredibly brave” @GaryMarcus.

In parallel, Marcus reiterated his view that GenAI is dangerous because it’s unreliable, especially if deployed in high-stakes military contexts without oversight @GaryMarcus.

Big Ideas

1) AI transformation has a predictable “messy middle” — plan for a productivity J-curve, not a straight line

A Product School talk frames AI adoption as a productivity J curve: an initial “fun/pilot” phase, a productivity decline when moving into production (requiring context, data governance, guardrails, systems, and feedback loops), and then a productivity spike that drives business outcomes Product School.

Why it matters: If you treat the dip as failure, teams revert to old workflows before the benefits show up.

How to apply:

1. Treat productionization work (governance + guardrails + feedback loops) as a first-class roadmap item, not “tech debt later” Product School.
2. Tie AI initiatives back to business outcomes (e.g., revenue growth, profit margin, product quality) rather than tracking “AI adoption” as a goal on its own Product School.

2) “AI-native” operating models are forcing org changes across people, tools, and leadership expectations

The same Product School session described a transformation that included:

• People changes: raising the hiring bar to “AI natives,” plus a claim that 1 AI-native person + agents can replace 2 non-AI-native employees without sacrificing productivityProduct School. Leadership was expected to be “builders” / player-coaches (not just managers) Product School.
• Tooling and workflow changes: revamping 66% of SaaS tools, including cutting ~1/3 with no replacement, replacing ~1/3 with AI-native tools, and replacing ~1/3 with products built in-house (“vibe coded”) Product School.

Why it matters: The pattern is less “add AI on top of existing process,” more “use AI to force a workflow rethink.” The tool swap was explicitly described as a way to avoid copy/pasting old processes into new tools Product School.

How to apply:

1. When changing tools, require teams to write down which workflow steps they will delete (not just migrate) Product School.
2. For leadership roles, explicitly assess the “builder” expectation (hands-on ability + judgment + influence + people management) rather than assuming the role is purely oversight Product School.

3) Human-in-the-loop (HITL) should be designed as a strategic allocation of judgment, not a permanent bottleneck

A CNN product/design leader describes a spectrum between control of outcomes and agency, with a general pull toward more agency (scaling quickly, personalization, new user value) but higher risk to user trust and brand integrity because agents are unpredictable Product School. Their core claim: the goal isn’t “zero human involvement,” but to allocate judgment where it counts so you can unlock agency without undermining trustProduct School.

Design framework (what to operationalize):

• When does a human step in? Triggers should be explicit and testable and optimized for product risk (impact if wrong), not the agent’s confidence Product School. Examples include external publication, one-way doors/irreversible actions, blast radius thresholds (users/revenue), regulated/sensitive domains, and scenario-based triggers Product School.
• What does the human do? Choose between binary review (fast/consistent; weak learning signal) and open-ended review (rich learning signal; expensive), often with hybrids/staging over time Product School.
• What happens after the human acts? Capture judgments into prompts/evals/training so HITL reduces future workload; repetition is a signal to encode feedback into the system Product School.
• How does it evolve? Define success criteria and explicit downgrade criteria so HITL doesn’t stick around indefinitely Product School.

Why it matters: Without clear triggers + downgrade criteria, “HITL” becomes an organizational default that slows shipping without improving the system Product School.

4) Product-led growth is shifting toward “agent-led growth” (distribution becomes the constraint)

One Product School framing: speed is no longer the bottleneck and “product is no longer the moat”; building digital products is easier than ever, while growth/distribution is harder Product School. The talk argues that in an “agent-led” world, product teams increasingly optimize for agents discovering the product (e.g., agent marketplaces as new app stores where agents evaluate tools by benchmarks/performance metrics) before humans do Product School.

Why it matters: If discovery and adoption move toward programmatic evaluation, traditional tactics like ads and polished landing pages matter less in some flows Product School.

How to apply:

1. Identify where your product could be evaluated by benchmarks/metrics rather than brand and storytelling, then prioritize what you can measure and improve Product School.
2. Treat “distribution” as an explicit constraint, not an assumption—especially if product build speed increases but adoption doesn’t Product School.

5) Strategy clarity increasingly shows up as storytelling discipline (internally and externally)

Hiten Shah’s framing: “Your story is your strategy, made legible.”@hnshah He argues that if you can’t clearly explain what your company does, you likely don’t have a clear strategy @hnshah. Storytelling forces clarity by revealing gaps when you try to explain the strategy to someone who doesn’t know the space @hnshah. At scale, inconsistent stories across leaders show up as misaligned roadmaps and confused customers @hnshah.

Why it matters: Misalignment isn’t just a communication problem; it becomes a delivery and prioritization problem @hnshah.

How to apply:

1. Write a one-paragraph “what we are / what we aren’t” narrative and pressure-test it with outsiders; use the gaps to surface unresolved strategy decisions @hnshah.
2. Repeat the story until teams can repeat it back (treat it as a scaling mechanism) @hnshah.

Tactical Playbook

1) Build a survey system that acknowledges low response rates (and still produces signal)

A recurring caveat from PMs: general surveys often get 2–5% response rates and skew toward people at the extremes r/ProductManagement. The suggested counter is a portfolio of smaller, better-timed surveys plus analysis discipline.

Tactics you can implement this cycle:

1. Continuous sentiment tracking (quarterly pulse to rotating cohorts): 3 questions (overall satisfaction, top pain point, biggest missing feature) and compare cohort-over-cohort rather than fixating on absolute scores r/ProductManagement.
2. Jobs-to-be-done discovery (1–2×/year): open-ended prompts like “What were you trying to accomplish when you last used [product]?” and “What almost stopped you?” r/ProductManagement.
3. Churn / friction moment surveys: trigger at cancellation, downgrade, or inactivity windows; these can have higher response rates and more actionable data than general satisfaction surveys r/ProductManagement.
4. Feature-specific micro-surveys: after a user completes a key workflow, ask “did this do what you expected?” and “what was confusing?”; use longitudinal comparisons to detect accumulating UX debt r/ProductManagement.

Analysis discipline (to avoid noisy decisions):

• Set a reading quorum: don’t report until you have N responses r/ProductManagement.
• Tag qualitative responses by theme (not just sentiment) r/ProductManagement.
• Cross-reference survey responses with behavioral data; divergence can indicate something important r/ProductManagement.
• Share raw anonymized quotes with engineers/designers (not just summaries) so the team internalizes the user’s language r/ProductManagement.

2) Prevent “late requirement discoveries” without crushing autonomy

A PM leader described a recurring delivery failure mode: late discoveries (edge cases, integration gaps, missing requirements, compliance nuances) that add scope, push timelines, and erode trust with engineering/stakeholders—often because the feature wasn’t fully thought through up front r/ProductManagement.

Coaching structure (simple, repeatable):

1. Past: ask why issues weren’t caught earlier r/ProductManagement.
2. Present: ask what they’re doing now to catch issues sooner r/ProductManagement.
3. Future: require systematic mechanisms (process + documentation like checklists) to catch them earlier next time r/ProductManagement.

Mechanisms that keep rigor high while preserving autonomy:

• Peer review discovery docs/hypotheses to challenge assumptions r/ProductManagement.
• Make risk tolerance explicit: some orgs prioritize value over exhaustive mitigation; others won’t accept the risk. Calibrate expectations accordingly r/ProductManagement +1.
• When perfectionism stalls delivery, explicitly document what’s accepted as a “problem for future us” to help teams descope toward 80–90% value sooner r/ProductManagement.
• For complex domains, consider using AI to generate edge cases from a PRD (as a supplement to human review) r/ProductManagement.

When it becomes performance management: One suggestion was that if the PM isn’t trying to improve (or the above isn’t working), escalation up to a PIP may be warranted r/ProductManagement.

3) Use “HITL triggers + review type” as a product decision (not just a compliance checkbox)

If you’re building agentic workflows, the CNN framework provides practical levers:

1. Define triggers based on risk and blast radius (impact if wrong), not model confidence Product School.
2. Choose the review type:
   • Binary when criteria are clear and you’re gating a specific action (fast/scalable) Product School.
   • Open-ended when nuance/values judgments matter and you want to improve the system over time (expensive but informative) Product School.
3. Treat human feedback as training signal: encode repetitive feedback into prompts/evals so workload declines over time Product School.
4. Define success + downgrade criteria up front so HITL doesn’t become permanent process debt Product School.

4) Interview like a “Context-Builder” (and avoid the two common failure personas)

A recruiter’s breakdown of four PM interview personas argues the one that gets offers is the Context-Builderr/ProductManagement.

The “Context-Builder” pattern (3 beats):

1. Clarify context (“Before I jump in, what I’d want to understand is…”) r/ProductManagement.
2. Give a crisp answer with a real example, decisions, and trade-offs r/ProductManagement.
3. Ask a thoughtful question back that creates collaboration r/ProductManagement.

Two failure modes to watch for:

• Talk-track derailers: don’t answer the question; fix by answering directly in 1–2 sentences, then adding context and asking permission before tangents r/ProductManagement.
• Framework warriors: when frameworks become the star instead of your thinking, founders can feel “handled”; use frameworks mentally as checklists, speak naturally, and adapt to the company stage r/ProductManagement.

Case Studies & Lessons

1) Wise: when opportunity sizing is based on unvalidated behavior assumptions, Excel lies

Wise’s CPO shared a case where 6% of card payments failed due to insufficient balance; the team built an “auto top up” feature and predicted ~10% more card volume, but adoption was closer to 1 in 10,000 users Product School.

Takeaway: “Obvious” value propositions can fail if the user behavior required (setup, trust, habit change) isn’t validated Product School.

2) Wise: small, fast customer testing can unlock outsized growth

A virality PM iterated a transfer-completion email mockup by showing versions to people in a coffee shop until it produced a visceral reaction; by credibly showing “real savings” (€20 vs. €6), referrals increased 300%Product School.

Takeaway: For growth work, the key question may be what customers believe, not what’s technically true in the product UI Product School.

3) Product Hunt launch: ranking creates visibility; onboarding + retention determines whether it matters

A startup’s Product Hunt launch lessons emphasized:

• What helped: clear 1-line positioning, showing real workflows (not feature lists), fast engagement in comments, and cross-platform support to build credibility r/startups.
• What didn’t: traffic didn’t equal retention; many users tested briefly and didn’t explore deeply; without pre-launch audience prep, early momentum was hard; launch-day excitement faded without a follow-up plan r/startups.

A follow-up comment cited outcomes from a separate Product Hunt launch: ~100 accounts directly from Product Hunt, ~300 new accounts after secondary mentions, “several paying customers,” and ongoing 1–3 new accounts/day plus “a customer every 2–3 days” weeks later r/startups.

Takeaway: Treat launch as a top-of-funnel spike; your “real work” is turning first-use into repeat use and retention r/startups.

4) Product School: shipping acceleration came from an “agentic workflow” for course updates

Product School’s CEO said they shipped more product in the last 6 months than the prior two years, launched AI-native courses (AI prototyping, AI evals, advanced agents), and credited a new agentic workflow that reduced course content upgrades from months/quarters to weeksProduct School.

They also described partnerships allowing members to use premium AI tools for free (including OpenAI, Lovable, Linear, Gamma, Replit, Miro, and others), described as worth over $10,000 on the market Product School.

Takeaway: The compounding advantage was not just “building AI content,” but building a workflow that reduced iteration time on the product itself Product School.

Career Corner

1) A PM GitHub is becoming a concrete “builder” signal (and a differentiator)

Aakash Gupta reports that only 24% of PM candidates have a GitHub Product Growth +1, while “every PM” he placed at OpenAI, Anthropic, and Meta AI in the last year did Aakash Gupta. He also reports hiring managers said that if a GitHub is linked, they will check itProduct Growth.

What it signals (per the post):

• You actually build things (not just talk about building) Product Growth.
• You understand how technical teams work Product Growth.
• You can navigate the tools engineers use daily Product Growth.

Example: Shubham Saboo’s GitHub helped him move from DevRel to Senior AI PM at Google Cloud, with the claim that inbound recruiting increases interview-to-offer rate from 22% to 37%Product Growth +1.

2) “PM” titles in big banks can mean P&L + operations management (and may not map cleanly to product building roles)

A thread on Canadian banks described many “PM” roles that are framed as product but cover responsibilities like credit card/portfolio P&L, investment returns, marketing programs, customer acquisition strategy, financial advisor relationships, and call center workforce management r/ProductManagement. One commenter argued “product manager” can be a framework for managing the direction/strategy of a revenue-generating entity r/ProductManagement, while another raised whether this would count as “general PM experience” when applying to roles requiring traditional frontend vs. infra/platform PM work r/ProductManagement.

How to use this: If you’re hiring, tighten your definition of the PM capabilities you need (product discovery/delivery vs. commercial/ops ownership) and screen accordingly r/ProductManagement +1.

3) If you’re not getting offers, shift from “answering” to “shaping the conversation” in interviews

If you tend to be a minimal “Question-Answerer,” the advice is to add (a) business context, (b) one key decision/trade-off, and (c) actual impact r/ProductManagement. If you’re tempted to lead with frameworks, keep them as internal checklists and speak naturally about constraints and first-principles reasoning r/ProductManagement.

Tools & Resources

• Beyond the Pilot: The AI-Native Product Operating Model (Product School, YouTube) — productivity J-curve; builders; shift toward agent-led growth; tool stack revamp Product School.

  • https://www.youtube.com/watch?v=ZuQHYTDDuTU

• Architecting Human-in-the-Loop Agentic Workflows to Scale Judgment (CNN, YouTube) — explicit/testable HITL triggers; binary vs open-ended review; feedback capture; downgrade criteria Product School.

  • https://www.youtube.com/watch?v=StMKWfYOVfE

• Manifesting the Future as a PM: How to Predict & Ship High-Impact Products (Wise, YouTube) — cautionary sizing example + referral lift case study Product School.

  • https://www.youtube.com/watch?v=_e-NwH9Csw4

• This Github Got a PM Hired at Google (Aakash Gupta, Substack) — PM GitHub adoption stats + what hiring managers look for Product Growth +1.

  • https://www.news.aakashg.com/p/you-should-build-a-pm-github

• Community threads worth skimming (for practitioner detail):

  • Survey methodology ideas (PM subreddit) r/ProductManagement
  • Preventing late requirement discoveries (PM subreddit) r/ProductManagement +1
  • Product Hunt launch lessons (r/startups) r/startups +1

Most compelling recommendation: The Hard Thing About Hard Things — operating psychology when the job gets brutal

The Hard Thing About Hard Things (book) — Ben Horowitz

• Content type: Book
• Author/creator: Ben Horowitz
• Link/URL: Not provided in sources (context: recommended in interviews)
• Who recommended it:
  • Jeetu Patel (on Lenny’s Podcast) Lenny's Podcast
  • Brian Halligan (in conversation with Horowitz) Sequoia Capital
• Key takeaway (as shared):
  • Patel highlighted the book as guidance for “how you manage your psychology when things get hard.”Lenny's Podcast
  • Halligan said he read it while running HubSpot, “thought it was really good,” and referenced the book’s stance that it’s “not a fan of the idea of hiring a COO.”Sequoia Capital
• Why it matters: This is a rare “two independent operator endorsements” pattern—and both endorsements point to the same underlying value: decision-making and leadership under stress, not in ideal conditions. Lenny's Podcast +1

Strategy classics that leaders treat as foundational

The Innovator’s Dilemma + The Innovator’s Solution (books) — Clayton Christensen

• Content type: Books
• Author/creator: Clayton Christensen
• Link/URL: Not provided in sources
• Who recommended it: Jeetu Patel Lenny's Podcast
• Key takeaway (as shared): Patel called these books “the Bible in Tech” and said he recommends re-reading them every few years.Lenny's Podcast
• Why it matters: A “re-read every few years” recommendation is a strong signal that the framework keeps paying off as markets, products, and roles change. Lenny's Podcast

Source context: https://www.youtube.com/watch?v=ylNKlBlkFasLenny's Podcast

Investing + career: staying fluid and using surprise as an input

Finite and Infinite Games (book) — James Carse

• Content type: Book
• Author/creator: James Carse
• Link/URL: https://www.generalist.com/p/infinite-gamesThe Generalist
• Who recommended it: The Generalist author (while announcing he’s joining Hummingbird as a partner) The Generalist
• Key takeaway (as shared):

• Why it matters: The author explicitly ties the book to a career philosophy centered on fluidity and “allowing oneself to be surprised,” describing his own best paths as a mix of “shock and inevitability.” The Generalist

Institutions, power, and governance: a regulator-focused reading pick

Reputation and Power (book) — Daniel Carpenter

• Content type: Book
• Author/creator: Daniel Carpenter
• Link/URL: Not provided in sources
• Who recommended it: Balaji Srinivasan On The Brink with Castle Island
• Key takeaway (as shared): Srinivasan called it a “great book” and noted it’s written by someone more sympathetic to the FDA than he is—then added that the FDA has “good aspects” and “bad aspects,” but these aren’t widely discussed, pointing out: “when’s the last time we elected the commissioner of the FDA?” On The Brink with Castle Island
• Why it matters: It’s recommended as a way to reason about regulators via reputation and institutional power, including views that differ from the recommender’s priors. On The Brink with Castle Island

Source context: https://www.youtube.com/watch?v=vGCqe2fMO6sOn The Brink with Castle Island

Political economy + elite psychology (two Balaji picks)

The Sovereign Individual (book)

• Content type: Book
• Author/creator: Not specified in sources
• Link/URL: Not provided in sources
• Who recommended it: Balaji Srinivasan On The Brink with Castle Island
• Key takeaway (as shared): He said he’s “sympathetic” to the sovereign individual thesis and likes the book quite a lot, while also saying he’s increasingly thinking about “the sovereign collective.” On The Brink with Castle Island
• Why it matters: Recommended as a serious framing—even as the recommender signals an active evolution of the thesis toward collective forms. On The Brink with Castle Island

Born Rich (documentary film) — Jamie Johnson

• Content type: Documentary film
• Author/creator: Jamie Johnson
• Link/URL: Not provided in sources
• Who recommended it: Balaji Srinivasan On The Brink with Castle Island
• Key takeaway (as shared): Srinivasan described it as an “interesting movie” about Jamie Johnson (described as an heir to the Johnson & Johnson family) exploring guilt about having lots of money.On The Brink with Castle Island
• Why it matters: A concrete, narrative-format recommendation for understanding how wealth and identity can interact—useful context for founders/investors who operate around extreme outcomes. On The Brink with Castle Island

AI in the real world: an unpolished user interview worth watching

“User Interview #3” (video) — Avi Schiffmann

• Content type: Short video (posted on X)
• Author/creator: Avi Schiffmann
• Link/URL: https://x.com/AviSchiffmann/status/2026798365489725742@garrytan
• Who recommended it: Garry Tan @garrytan
• Key takeaway (as shared):
  • Tan emphasized it’s “not the happy demo path” and “it’s real.” @garrytan
  • The punchline he pulled out: “AI: Doesn’t get tired, doesn’t ghost.”@garrytan
• Why it matters: A strong signal for people tracking AI adoption: the recommendation is explicitly about authentic usage and behavior in practice, not launch-video polish. @garrytan

Resurfaced signal: founder writing clarity as a diligence input

Amazon’s “’97 shareholder letter” (shareholder letter) — Jeff Bezos

• Content type: Shareholder letter
• Author/creator: Jeff Bezos
• Link/URL: Not provided in sources (context: discussed in an X post)
• Who recommended it: Patrick O’Shaughnessy, relaying Dan Sundheim (D1 Capital; “manages over $30B across public and private markets”) @patrick_oshag +1
• Key takeaway (as shared): The post argues that founders who can write clearly are “rare,” and claims that “the only signal that mattered early in Amazon was the ’97 shareholder letter.” @patrick_oshag
• Why it matters: This is another instance of long-form writing being elevated as an early, high-signal artifact when other signals may be noisy. @patrick_oshag

Source post: https://x.com/patrick_oshag/status/2027057100979859525@patrick_oshag

1) Market Movers

Oilseeds: soybeans hit fresh highs, then reversed on China/trade uncertainty

• Feb 26 pricing (international): soybeans down 0.17% to $11.63/bu, while corn and wheat finished higher Canal Rural.
• Farm Journal’s close described soybeans making new highs then “crashing,” tied more to China concerns than month-end profit taking—amid talk that U.S.-China negotiations were “not going well” and with the reminder that Brazilian beans are about $1 cheaperFarm Journal.
• The same segment also flagged that exports were “dismal” and that the U.S. is “too expensive” on the world market, making it hard to sustain upside momentum Farm Journal.

Soybean oil & biofuels: policy optimism vs. heavy inventories

• Farm Journal’s early show emphasized optimism tied to an RVO proposal moving through regulatory channels, while noting crush pace has been below what’s needed for USDA’s forecast and soy oil stocks have climbed for three consecutive monthsFarm Journal.
• Discussion of 45Z included: prioritizing North America (including Canadian canola oil), and removing the indirect land use penalty that had been “penalizing row crop” from a carbon-emissions standpoint—framed as supportive for soybean oil relative to the prior 18 months Farm Journal.

Corn: strong demand narrative, but watch basis and acreage

• Farm Journal’s close framed corn as a split story: cash corn potentially struggling versus Dec futures staying “relatively supported” until acres are secured, with basis a key risk in rallies Farm Journal.
• The same segment cited U.S. demand at 16.47B bu (attributed to USDA), calling it a historically large demand figure Farm Journal.
• Export signal: USDA reported 178,000 MT of corn sold to Japan for delivery in MY 2026/27 (154,000 MT) and MY 2027/28 (24,000 MT)@USDAForeignAg.

Wheat: weather-driven premium fading after a pop

• Farm Journal described wheat consolidating after a “big pop,” with last week’s rally attributed partly to weather premium, but with a more favorable rainfall forecast weighing on follow-through Farm Journal.
• In a separate Farm Journal segment, wheat’s outlook was framed as difficult with positive weather in the forecast for Kansas HRW areas and “the world has plenty of wheat,” implying export-side surprises would matter most Farm Journal.

Livestock: cattle risk headline—JBS strike talk

• Farm Journal’s close flagged strike whispers at the JBS plant in Greeley as a bigger story than month-end profit taking, suggesting it could create sharp downside risk and reinforcing the need to protect cattle price risk near recent highs Farm Journal.

Brazil cash snapshots (Feb 26)

• National physical quotes (selected): soybeans in Rio Grande do Sul at R$122/saca (+R$1), corn in RSR$53/saca (stable), and wheat in ParanáR$1,200/ton (stable) Canal Rural.

2) Innovation Spotlight

Burndown ROI and operational flexibility (U.S.): Reviton programs in no-till

• Product + fit: Reviton (saflufenacil; Group 14 PPO) was positioned as a flexible burndown option in reduced/no-till systems, controlling 50+ broadleaf and grass species, including some glyphosate-, ALS-, and triazine-resistant weeds, and functioning under cool conditions No-Till Farmer.
• Cost math cited in the training: a proactive Reviton program was described as a $5–$10/acre add-on that can avoid rescue passes costing $30–$50/acre, for stated net savings of $15–$40/acre (chemistry + fuel + yield protection) No-Till Farmer.
• Operational note: updated label guidance emphasized rapid plant-back intervals (e.g., 0-day for corn; 0–7 days for soybeans depending on rate/conditions) to “keep the planter moving” No-Till Farmer.

Precision decision support: AI-based fungicide timing

• Pioneer launched an AI-based fungicide timing tool intended to predict disease risk and identify optimal spray windows using field-level data@SuccessfulFarm.

Connected-farm experimentation: Starlink for irrigation + sensing

• Nick Horob described plans to irrigate trees/small plots on an arid 500-acre farm using a productive well and Starlink-connected pumps and switches for remote operation @NickHorob.
• He also plans to extend Starlink across the farm to support soil moisture sensors, cameras, and other experiments, with the intention to share results and possibly live data feeds @NickHorob.

Post-harvest automation: bin monitoring to protect grain quality

• An Indiana farmer highlighted an AGI 137,000-bushel bin and a bin manager system that can automatically run fans to manage moisture/temperature risk, framed as protecting $600k–$700k of grain value in a single bin and reducing “risk factor” and quality anxiety Brownfield Ag News.

On-farm feed manufacturing: grinder mixers to reduce custom costs

• Art’s-Way’s largest grinder mixer (model 8215) was described as a 215-bushel tank, 26-inch hammer mill, roughly 6-ton capacity unit (diet-dependent), requiring 90–100 PTO HPFarm4Profit Podcast.
• Cost lever cited: avoiding $10–$16/ton custom grind/mix delivery by using more grain on-farm with less handling/transport Farm4Profit Podcast.

Small-/mid-scale tech adoption (Brazil): drones + automation with payback windows

• A Canal Rural segment said replacing conventional sprayers with drones can eliminate trampling (“amassamento”) losses and recover productivity in tramlines from the first harvestCanal Rural.
• It also pointed to automation options that can pencil for smaller farms (e.g., pumping/irrigation and water supply for small animals) Canal Rural and noted that automated feeding systems for small animals can show better economic consistency after 4–5 cycles, assuming management adjustments accompany the tech Canal Rural.

3) Regional Developments

Brazil (Mato Grosso + Northern Arc): rain + logistics disrupting soybean harvest execution

• Field conditions: reports cited 2,300 mm of accumulated rain versus an 1,800 mm annual average, with harvesters getting stuck and conditions also complicating corn planting Canal Rural.
• Crop progress and losses: Mato Grosso soybean harvest was cited at 66% complete, with corn planting at 65% by Feb 20, both slowed by sustained rainfall; estimated soybean losses were discussed at ~15% in affected areas Canal Rural.
• Export bottleneck: the road access to Miritituba (Pará) port terminals was linked to truck queues up to 30 km, driven by poor road conditions (worse when it rains), leaving terminals short of trucks and slowing discharge despite daily targets Canal Rural.

• Economic impact signals: producers described losing 10–15 BRL per soy sack due to logistics delays Canal Rural. Another report highlighted BR-163 challenges (including a toll cited at R$676.80) alongside potholes/accident risk, framed as compounding transport difficulty during peak harvest Canal Rural.

Mercosur–EU agreement: ratification pace hinges on safeguards

• Status: the agreement was approved in the lower houses of Brazil and Argentina, while Brazil’s Senate was described as awaiting a government safeguards decree before taking the text to a plenary vote Canal Rural.
• Safeguards definition in the reporting: tools include import quotas, suspending tariff reductions, and restoring prior tax levelsCanal Rural.

Brazil rice: commercialization support tied to falling prices

• Conab announced releasing R$73M+ to support rice commercialization for the 2025/26 crop, with an estimate that it could support around 300,000 tons depending on premium size; R$61.3M was allocated to Rio Grande do Sul, linked to moving about 250,000 tonsCanal Rural.

Animal health trade impact: Argentina avian influenza

• Argentina’s SENASA confirmed a highly pathogenic avian influenza case in Buenos Aires province, triggering quarantine/disinfection/culling and a suspension of poultry product exports to trading partners Canal Rural.

U.S. conditions watch: moisture + animal health

• Iowa drought: moderate drought was reported to jump from 11% to 25% in a week (February U.S. Drought Monitor framing) as planting season approaches @SuccessfulFarm.
• Iowa poultry: the Iowa Department of Agriculture and Land Stewardship reported two HPAI cases in flocks in Keokuk and Van Buren counties @SuccessfulFarm.

Soft commodities: Ghana cocoa financing stress

• A Reddit post flagged reports that Ghana cocoa buyers may owe banks up to $750M, framed as financing stress that could tighten forward selling/hedging capacity and exporter liquidity, with potential price impacts showing up with a lag r/AgriTech.

4) Best Practices

Weed control in tight spring windows (no-till burndown)

• Timing priority under cold/wet conditions: in a Q&A, the guidance was to prioritize weed size and forecast (treated as “one and one”) ahead of other factors when windows are tight No-Till Farmer.
• Species-specific timing examples: ryegrass control was framed as best at the 1–2 leaf stage, while henbit can be controlled later (up to bloom) No-Till Farmer.
• Spray setup details cited: increase carrier volume to 15–20 GPA, use a full MSO load (1% v/v), and avoid large-droplet nozzles—using smaller droplet sizing such as a flat-fan nozzle in residue/cool conditions No-Till Farmer.

Balancing burndown timing with soil biology (cover crops)

• A no-till training emphasized that burn down timing “really does matter” because when living roots disappear, microbial activity can drop quickly; later termination was framed as supporting nutrient cycling closer to crop demand and reducing erosion risk on highly erodible soils No-Till Farmer.

Winter grazing systems: stockpiling and targeted hay “unrolling”

• A Missouri grazing strategy described using “one bite and leave” through the season to thicken the grass sward and stockpile the entire farm by fall Practical Farmers of Iowa.
• Winter management included rotating twice daily on stockpiled forage (rain-dependent), and using purchased hay placed strategically so not all of it is eaten—leaving some residue for soil microbes Practical Farmers of Iowa.

Pastured poultry: reducing predation pressure with layered defenses

• Practical tactics cited included guard animals (including aggressive geese) Douglas Holloway, moving egg mobiles/netting to disrupt predator routines Douglas Holloway, and electric netting paired with short-grass “DMZ” zones to deter smaller predators Douglas Holloway.

5) Input Markets

Fertility cost pressure (Brazil): rising fertilizer costs vs. weaker soybean prices

• In Mato Grosso, one segment described fertilizer costs (N/P/K) running ~25% higher versus the prior season, while soybean prices were described around ~R$100/saca, down from R$170–R$180 during the pandemic period Canal Rural.

Biofuel-driven demand debates: soybean oil needs “clear” demand signals

• One Farm Journal segment said crush pace has been below the pace needed for USDA’s forecast while soy oil stocks climbed for three straight months, arguing the market needs positive policy/demand news that prioritizes domestic feedstocks to help draw down inventories Farm Journal.

Crop protection and trait positioning

• Syngenta’s DuraStack trait technology was promoted as featuring three modes of action and a triple Bt protein stack for corn rootworm control, with rootworm costs cited at up to $1B/year and availability framed for the 2027 seasonFarm Journal.

Feed manufacturing + quality variability

• A sugar beet processing challenge was described: some areas saw very high tonnage (30–40 tons/acre) but low sugar (15–16%) versus a traditional 20% sugar target, which was described as inefficient for co-ops processing beets Farm4Profit Podcast.

6) Forward Outlook

Weather windows (Brazil): near-term fieldwork vs. incoming rains

• Canal Rural weather coverage described the Centro-Sul as currently hot/dry (especially Mato Grosso do Sul and interior São Paulo), with more substantial rains expected from next weekend and stronger volumes in the 9–13 March window for areas including Paraná, interior SP, and MS Canal Rural.
• The same coverage called out >100 mm rains forecast for parts of Brazil’s interior Northeast (BA/MA/PI/PE interior), framed as helpful for reversing water deficits Canal Rural.

2026 acreage direction (U.S.): “soybean swing” vs. corn pullback

• A Farm Journal discussion suggested that a 94M corn-acre number would be viewed as “very friendly,” but argued corn acres likely back off from outlier years (with crop insurance economics still a factor) Farm Journal.
• Another Farm Journal source forecasted soybean acres at 86M (vs. USDA’s 85M and 81.2M last year) and corn acres at 94M, tied to price ratios, renewable fuel buildout expectations, and farmer-held unsold corn stocks Farm Journal.

Logistics as a continuing price/spread variable (Brazil)

• In Mato Grosso’s northern route, one report warned harvest is still early—“not 5% of areas” have moved through the route yet—implying congestion risk remains as volumes build Canal Rural.

Items to monitor

• China-driven soybean volatility: the same factors that drove Feb 26’s reversal (China concern + U.S. price competitiveness) remain a headline risk Farm Journal.
• Livestock disruption risk: strike talk at major packing plants was framed as a sudden downside catalyst for cattle markets Farm Journal.