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

🔥 TOP SIGNAL

A practical pattern is crystallizing for long-running coding agents: treat context + memory as a filesystem the agent can browse on-demand, rather than a one-time prompt dump. @koylanai lays out a concrete design (namespaced /context/*, runtime list/read/search, per-turn manifests of what was loaded/skipped, and a scratchpad→episodic→fact memory lifecycle) that specifically targets compaction loss and “silent” memory failures—and claims it’s been validated in production via Cursor’s file-based “dynamic context discovery.” @koylanai

🛠️ TOOLS & MODELS

• Showboat ecosystem expands (Simon Willison)

  • Showboat v0.6.0 “remote”: set SHOWBOAT_REMOTE_URL and every showboat init/note/exec/image POSTs document fragments to your endpoint for live viewing while the agent works. simonwillison
  • New companion tools:
    • Chartroom: CLI charting (matplotlib wrapper) designed to generate charts that can be embedded in Showboat docs. simonwillison
    • datasette-showboat: Datasette plugin with /-/showboat viewer + /-/showboat/receive endpoint to receive streamed updates. simonwillison
  • Details: https://simonwillison.net/2026/Feb/17/chartroom-and-datasette-showboat/@simonw

• Claude Code for web + Desktop workflow (Simon Willison)

  • He uses Claude Code on the web (container-managed environment) but accesses it via iPhone/Mac desktop apps. simonwillison
  • Tip: if Claude Code is looking at screenshots, you can view those screenshots yourself in the desktop chat transcript. @simonw

• Model speed/cost trade-off (Armin + Ben)

  • Claude “Fast mode”: reported as 2.5× throughput for 6× cost, and Armin says he burned the included $50 in a single session. Armin Ronacher
  • Codex 5.3: Armin says he uses 5.3 “exclusively” because it feels noticeably faster, without seeing a programming-quality jump. Armin Ronacher

• Standards & UX nits that keep coming up

  • agents.md standardization: Theo complains about having to symlink Claude.md to AgentsMD, and says OpenAI is moving “everything to AgentsMD.” Theo - t3․gg
  • Antigravity UI editing: demo suggests selecting an area on a screenshot and describing layout changes—positioning it as a faster interface than writing long text specs. @antigravity

💡 WORKFLOWS & TRICKS

• Make context assembly debuggable (filesystem + manifests)

  • Core move: put everything under a predictable /context/ namespace and give the agent runtime file ops (list/read/write/search), so it can discover what exists before loading it. @koylanai
  • Add an operational layer:
    1. Constructor: selects/compresses context into a token-budget input and emits a manifest (what was selected/excluded and why). @koylanai
    2. Updater: streams extra context during the turn, swapping pieces based on feedback instead of stuffing everything at boot. @koylanai
    3. Evaluator: checks outputs against sources, writes verified info back as structured memory, and flags human review when confidence is low. @koylanai
  • Memory lifecycle that avoids “one forever file”: split scratchpad / episodic / fact, with promotion + retention policies. @koylanai

• Non-breaking migration path for existing agents (koylanai)

  1. Start returning file references before snippets + emit per-turn load manifests. @koylanai
  2. Expose /context/* and allow runtime list/read so the agent can browse without loading everything. @koylanai
  3. Switch to minimal preload + on-demand fetch; decompose MEMORY.md. @koylanai
  4. Add promotion/archival/retention policies and audit logs for reversible state transitions. @koylanai

• Live “agent work log” you can review while it’s working (Showboat remote + Datasette)

  • Run Datasette with the plugin, then point Showboat at it:

    uvx --with datasette-showboat --prerelease=allow 
      datasette showboat.db --create 
      -s plugins.datasette-showboat.database showboat 
      -s plugins.datasette-showboat.token secret123 
      --root --secret cookie-secret-123

    simonwillison

    export SHOWBOAT_REMOTE_URL="http://127.0.0.1:8001/-/showboat/receive?token=secret123"

    simonwillison

  • Simon’s take: this is “wildly useful” for Claude Code for web—he can give feedback mid-flight based on screenshots being published live. simonwillison

• Browser/manual UI testing loop with screenshots (Rodney + Claude Desktop)

  • Prompt pattern: have the agent read uvx rodney --help, then use Rodney to click around and screenshot pages. simonwillison
  • Example commands used in a session: open a page, click a selector, screenshot to /tmp/menu.png, then Read /tmp/menu.png to analyze it—while you can also see the same screenshot in Claude Desktop. simonwillison +1

• “Zero context overhead” conditional prompting (Armin’s pi extension)

  • His go-to-bed.ts injects hidden prompts only after bedtime, so it’s “zero context overhead unless it’s late at night” (implemented with an “echo hack” due to limited bash tool hooks). @mitsuhiko

• Hands-off self-repair via SSH (steipete)

  • When ClawdHub errored, Peter says “claw” fixed it while he was at the barber: it SSH’ed into his MacBook Pro and deployed there because auth wasn’t configured on the agent’s host machine. @steipete

👤 PEOPLE TO WATCH

• @koylanai — clearest end-to-end proposal today for agent memory/context reliability: filesystem namespace + manifests + constructor/updater/evaluator + lifecycle policies. @koylanai
• Simon Willison — shipping developer-grade “agent observability” tooling (Showboat remote streaming + charting + Datasette viewer) and concrete Claude Code desktop workflows. simonwillison +1
• Armin Ronacher — practical extensions that modify agent behavior with minimal context cost (bedtime injection) and experiments in inter-session orchestration. @mitsuhiko +1
• @steipete (Peter Steinberger) — rare, concrete demo of a personal agent doing remote deploy/self-repair via SSH. @steipete
• Andrej Karpathy — high-signal framing: LLMs are especially strong at translation because the existing codebase becomes a detailed prompt + test reference. @karpathy

🎬 WATCH & LISTEN

• Fast mode reality check (State of Agentic Coding #3, ~37:39–39:36)
  • Hook: what “2.5× faster” actually means in practice when it costs 6×, plus the “burned $50 in one session” datapoint. Armin Ronacher

• Build a transcript-extractor skill with an external API (Riley Brown, ~10:55–22:09)
  • Hook: end-to-end skill creation + testing loop using Supadata—then using the transcript to generate summaries and drop them into Notion (including a full transcript). Riley Brown

📊 PROJECTS & REPOS

• Showboat family (Simon Willison): Showboat, Rodney, Chartroom, datasette-showboat (linked together as a loose-conventions ecosystem). simonwillison
• Rodney --help designed for agents: https://github.com/simonw/rodney/blob/main/help.txtsimonwillison
• Armin Ronacher’s pi extensions
  • go-to-bed.ts: https://github.com/mitsuhiko/agent-stuff/blob/main/pi-extensions/go-to-bed.ts@mitsuhiko
  • control.ts (messaging/orchestration experiments): https://github.com/mitsuhiko/agent-stuff/blob/main/pi-extensions/control.ts@mitsuhiko

— Editorial take: Today’s theme is agent observability: manifests, streamed work logs, and screenshot-based feedback loops are becoming the difference between “cool demo” and “debuggable daily driver.”

Top Stories

1) Qwen3.5’s open-weight release becomes a cross-stack default (model + inference tooling + hosted endpoints)

Why it matters: A strong open-weight model only reshapes the landscape when it’s easy to run (local + cloud) and supported by the major inference stacks.

Alibaba released Qwen3.5-397B-A17B, the first open-weight model in the Qwen 3.5 series, positioned as a native multimodal model trained for real-world agents and licensed under Apache 2.0@Alibaba_Qwen. Multiple posts emphasize the architecture: hybrid linear attention + sparse MoE with 397B total parameters / 17B active, targeting throughput and latency @vllm_project.

Distribution and “day-0” enablement showed up quickly:

• Inference stacks: day-0 support in vLLM (with deployment recipes) @vllm_project and SGLang (with cookbook + PR) @lmsysorg.
• Hardware ecosystems: AMD published day-0 guidance for Instinct GPUs via SGLang/vLLM @AIatAMD. NVIDIA highlighted a free build surface and a NeMo fine-tune config @NVIDIAAIDev.
• Hosted endpoints: Together AI listed it as production-ready with a 99.9% SLA and highlighted 87.8% MMLU-Pro plus “early fusion” multimodality @togethercompute. OpenRouter also added Qwen3.5-397B-A17B and Qwen3.5 Plus variants @OpenRouterAI.
• Local options: Unsloth published local run artifacts (including a GGUF) and claimed 4-bit can run on 256GB RAM@UnslothAI. Ollama made it available on cloud with ollama run qwen3.5:cloud@ollama.

Context + efficiency were recurring themes: Qwen3.5 Plus is described as the hosted API variant of the 397B model with 1M context (vs native 256K) plus search and code interpreter @JustinLin610. Separately, an analysis of Qwen3.5’s KV-cache footprint at 262K context estimates 8.05 GB in BF16 (or 4.025 GB in FP8 KV) and attributes it to the use of 45 gated deltanet layers@bnjmn_marie.

Pricing sparked debate. Novita listed $0.6/M input and $3.6/M output tokens @novita_labs, while another post noted a China vs international API price delta (e.g., $0.12 prefill + $0.69 decode in China vs $0.4/$2.4 internationally) @sun_hanchi.

2) “Open model week” continues: MiniMax M2.5’s agentic coding positioning turns into usage share

Why it matters: Popularity signals (usage, partners, integrations) often matter as much as model evals when developers choose defaults.

MiniMax M2.5 is repeatedly framed as strong for agent workflows. MiniMax says it uses per-token process rewards to better utilize signal across reasoning steps @MiniMax_AI, and a separate post claims “frontier coding performance” at at least 1/10th the cost of closed-source models @basetenco.

Adoption signals:

• OpenRouter reported M2.5 became the most popular model and hit #1 on the weekly leaderboard in four days@MiniMax_AI +1.
• Together AI announced MiniMax M2.5 availability for production-scale agentic workflows @togethercompute +1.
• Baseten listed M2.5 on its model APIs @basetenco.
• Windsurf added GLM-5 and MiniMax M2.5 with limited-time credit discounts @windsurf.

3) Inference economics take center stage: Blackwell Ultra claims, cross-vendor benchmarking, and memory constraints

Why it matters: For agents and long-context workloads, cost-per-token and performance-per-watt increasingly set the ceiling for deployment.

NVIDIA highlighted Blackwell Ultra GB300 NVL72, claiming up to 50× higher performance per megawatt (also framed as “tokens per watt”) and 35× lower cost per token versus Hopper, aimed at low-latency and long-context agentic use cases @kimmonismus +2.

In parallel, SemiAnalysis promoted InferenceX v2 benchmarks comparing Blackwell vs AMD vs Hopper, covering systems like GB300 NVL72, MI355X, B200, H100, and techniques such as disaggregated serving and wide expert parallelism, tested across SGLang, vLLM, TRTLLM@SemiAnalysis_.

Supply-side constraints also surfaced:

• Western Digital reportedly sold out its entire 2026 hard drive capacity, with most supply locked by top enterprise customers (consumers ~5% of revenue) @kimmonismus.
• A Bloomberg-linked thread described a growing memory chip crisis, with Sony considering pushing PS6 to 2028 or 2029@tomwarren.

4) Agent reliability and efficiency become the research focal point (benchmarks + pruning + adaptive reasoning)

Why it matters: As agents move into longer-horizon workflows, the bottleneck shifts to multi-step execution quality and wasted tool calls/tokens.

Notable signals this cycle:

• WebClipper models web-agent search as state graphs and prunes into minimal DAGs, reporting ~20% reduction in tool-call rounds while maintaining or improving accuracy; it also introduces F-AE Score to balance accuracy and efficiency in trajectories @dair_ai.
• CogRouter dynamically adjusts reasoning depth step-by-step across four cognitive levels; the report cites a 7B model reaching 82.3% success on agent benchmarks while using 62% fewer tokens than a baseline it outperformed (GPT-4o is named in the post) @omarsar0.
• SciAgentGym evaluates multi-step scientific tool use with 1,780 tools across 4 disciplines; a post claims success drops from 60.6% to 30.9% as interaction steps increase, and presents SciForge (dependency-graph trajectory synthesis) with an SciAgent-8B outperforming much larger models on scientific workflows @dair_ai.

5) OpenAI adds an enterprise security posture for ChatGPT workflows

Why it matters: Prompt-injection and connected-app risks are increasingly operational issues; “security modes” change what’s viable for regulated deployments.

OpenAI introduced Lockdown Mode for ChatGPT (initially for enterprise/business users), disabling some tools/capabilities that could be exploited to exfiltrate sensitive data via attacks such as prompt injection, including switching to cached browsing and limiting broader web interaction @cryps1s.

Research & Innovation

Why it matters: The highest leverage work right now targets faster inference (one-step generation, sparse routing), better reasoning under constraints (few-parameter adaptation, RL objectives), and agent training that wastes fewer steps.

Highlights from recent papers (curated list)

• Generative Modeling via Drifting: a generative framework that evolves the pushforward distribution to enable native one-step inference, reporting FID 1.54 (latent) / 1.61 (pixel) on ImageNet 256×256 @TheAITimeline.
• TinyLoRA (“Learning to Reason in 13 Parameters”): scales low-rank adapters down to a single parameter; the post claims 91% GSM8K accuracy with 13 trained parameters and recovery of 90% of gains on AIME/MATH500 while training 1000× fewer parameters than typical LoRA approaches @TheAITimeline.
• Maximum Likelihood Reinforcement Learning: defines compute-indexed objectives interpolating RL and exact maximum likelihood; claims up to 20× test-time scaling efficiency gains over GRPO in math reasoning and code generation tasks @TheAITimeline.
• Kimi K2.5 (agentic multimodal training): combines text/vision training stages and a parallel “Agent Swarm” orchestration approach; claims 4.5× latency reduction vs single-agent baselines @TheAITimeline.
• Generative meta-models of LLM activations: trains diffusion models on 1B residual stream activations to learn priors over internal states; reported to improve fluency for steering interventions and to scale sparse probing as neurons isolate concepts @TheAITimeline.
• On-Policy Context Distillation (OPCD): trains on self-generated trajectories while minimizing reverse KL vs a context-conditioned teacher; claims improved math reasoning/text-games accuracy and stronger OOD performance, supporting cross-size distillation @TheAITimeline.
• SkillRL: builds a hierarchical skill library (SkillBank) from trajectories using retrieval + recursive evolution; claims +15.3% on ALFWorld/WebShop and reduced token footprint @TheAITimeline.
• Retrieval-aware distillation for Transformer–SSM hybrids: keeps only attention heads critical for in-context retrieval; claims retaining 2% of heads recovers 95%+ of teacher performance, enabling 5–6× memory efficiency in retrieval-heavy settings @TheAITimeline.
• ViT-5: modernizes ViT with changes to normalization/activation/gating while preserving Attention–FFN layout; claims 84.2% ImageNet-1k top-1 accuracy and FID 1.84 in an SiT diffusion framework @TheAITimeline.

Additional notable research signals

• Deep-Thinking Ratio (DTR) and Think@n: proposes a measure of “deep thinking” effort and a test-time strategy that prefers/aggregates higher-DTR generations while stopping early on low-DTR ones; claims to outperform self-consistency using ~50% less compute@WeiLin__Chen +1.
• MonoLoss: a plug-in objective for SAEs that rewards semantically consistent activations to increase monosemanticity (MonoScore) across SAEs trained on CLIP/SigLIP2/ViT features @iScienceLuvr.
• Skills as procedural knowledge: a benchmark across 86 tasks / 11 domains and 7,300+ trajectories reports curated skills improve pass rates by 16.2pp on average, while self-generated skills show no average benefit; concise skills outperform comprehensive docs @omarsar0.

Products & Launches

Why it matters: Agents and models become “real” when they’re packaged into deployable workflows: chat surfaces, developer tooling, hosted inference, and observability.

Agents in mainstream chat surfaces

• Manus Agents launched inside chat apps (starting with Telegram), offering long-term memory, multi-step execution, and tool integrations (Gmail, Calendar, Notion, etc.) @ManusAI +1. One post also claimed this helps explain Meta’s acquisition of Manus (attributed as commentary) @kimmonismus.
• SkyBot (Skywork) is positioned as a cloud-native agent for long-term task execution with zero setup (no code/keys/servers), running in the background and accessible via phone/Discord/Telegram @kimmonismus +1.

Voice and realtime interaction tooling

• NVIDIA PersonaPlex is live on fal as a full-duplex model that listens and speaks simultaneously, handles interruptions/backchannels, and aims for low-latency spoken interaction with a consistent persona @fal.

Agent harness + “background agent” infrastructure

• Ollama added subagents and web search in Claude Code, enabling parallel tasks in isolated contexts (file search, code exploration, research) and automatic web search via the Anthropic compatibility layer—no MCP servers or API keys required @ollama +1.
• Terminal Use (YC launch) provides infrastructure for background agents, including filesystem forking and parallel agent runs; YC’s framing emphasizes that agent apps often “win on the harness” rather than the model alone @ycombinator.

Developer tools and document workflows

• LlamaIndex / LlamaCloud released a parsing feature to convert complex PDFs (tables, charts, multi-column layouts) into clean markdown/JSON via clickable templates @jerryjliu0.
• Base44 launched a standalone backend (CLI-first, realtime, AI-agent friendly) for deploying auth/database/hosting from the CLI @MS_BASE44.
• Synthesia launched a Word-to-video flow (upload → adjust settings → generate video; optional brand kit/translation/interactivity) @synthesiaIO.

Industry Moves

Why it matters: Usage momentum, distribution partnerships, and supply constraints influence which models and tools become defaults.

• Codex usage momentum: OpenAI’s Sam Altman said Codex weekly users have more than tripled since the beginning of the year @sama.
• Anthropic expansion: Anthropic opened a Bengaluru office, calling India its second-largest Claude.ai market @AnthropicAI.
• Model availability as a differentiator: Artificial Analysis benchmarked Kimi K2.5 across 8 providers, showing output speed variation of ~330 tokens/s, plus differences in latency (TTFAT/TTFT), pricing, context support, and multimodality/tooling support @ArtificialAnlys.
• Autonomy in the physical world: Waymo began full autonomous operations with its 6th gen platform; one post claims Waymo does 500,000+ driverless rides/week, and cites a ~$70k per-vehicle cost with room to fall by ~50% over two years @reed +1.

Policy & Regulation

Why it matters: As deployments expand, governance shows up as procurement rules, contract terms, transparency norms, and security controls.

• Pentagon–Anthropic tension (Axios-linked): posts report the Pentagon said Anthropic will “pay a price,” while Anthropic is described as willing to loosen terms of use but seeking safeguards against mass surveillance of Americans and fully autonomous weapons @unusual_whales +1. Another Axios-linked post says the Pentagon is considering labeling Anthropic a “supply chain risk,” which could force vendors to cut ties @DavidLawler10.
• Peer review integrity: a post claims ICML journal editors inserted hidden prompt injections into papers to detect AI-assisted reviewing, causing at least one reviewer to consider desk rejection after discovering it @paul_cal.
• Labor-market signal: one post claims the US BLS revised 2025 job numbers downward by over 1 million, with the Information sector revised down 88,000 jobs (3%), attributed by economists in the post to AI automation of tech-heavy roles @kimmonismus.

Quick Takes

Why it matters: Smaller signals often become the next “normal”—or the next operational risk.

• ByteDance BitDance: an open-source autoregressive image model with a GitHub repo and details like up to 32× downsampling and a codebook size up to 2^256@aisearchio +1.
• Chinese humanoid robots (Spring Festival Gala): Unitree showed a large robot cluster; posts highlight an autonomous Kung Fu performance and an H2 “Monkey King” segment with robot dogs @XRoboHub.
• Qwen3.5 in evaluation venues: LM Arena added Qwen3.5-397B-A17B to Text/Vision/Code arenas and asked users to test and vote for leaderboards @arena.
• Anthropic research on skill retention: a post summarized an RCT where AI coding assistance decreased skill mastery by 17% among 52 software engineers, with debugging most affected despite minimal productivity gains @dl_weekly.
• Meta patent: a post claims Meta patented an AI that takes over a deceased person’s account to keep posting and chatting @kimmonismus.

Blackwell Ultra pushes agent economics: 50× throughput-per-megawatt, 35× lower token costs

NVIDIA: GB300 NVL72 + software stack targets low-latency, long-context agents

New SemiAnalysis InferenceX data highlighted by NVIDIA says GB300 NVL72 systems (Blackwell Ultra) can deliver up to 50× higher throughput per megawatt and 35× lower cost per token vs. Hopper, attributed to hardware–software codesign (including NVIDIA Dynamo and TensorRT-LLM) nvidia. NVIDIA also points to TensorRT-LLM improvements delivering up to 5× better performance on GB200 for low-latency workloads compared with four months ago nvidia.

Why it matters: Agentic coding and interactive assistants compound latency across multistep workflows, so the focus on low-latency + long-context economics is aimed at making “always-on” agents viable at scale nvidia.

Deployment signals (and what’s next)

NVIDIA says Microsoft Azure, CoreWeave, and Oracle Cloud Infrastructure are deploying GB300 NVL72 for low-latency and long-context use cases like agentic coding and coding assistants nvidia. Looking ahead, NVIDIA says the Rubin platform (described as combining six new chips “to create one AI supercomputer”) is set to deliver up to 10× higher MoE inference throughput per megawatt than Blackwell nvidia.

Why it matters: If these deployment claims hold, the “agent layer” push is increasingly tied to infrastructure rollouts, not just model releases nvidia.

Agents: capability jump meets practical security and memory design constraints

Security alert: 1-in-286 public agent “skills” found malicious

A large-scale security audit of public AI agent skill repositories reported 1-in-286 skills were malicious, with scripts designed to exfiltrate .env files and local API keysr/LocalLLM. Reported common vectors included unauthorized os.environ reads during routine tasks and authority hijacking via fake [SYSTEM] headersr/LocalLLM. A free scanner was released at https://agentshield.liver/LocalLLM.

Why it matters: As agents become more tool-enabled, “skills” and tool descriptions become part of the supply chain—and a direct target for credential theft r/LocalLLM.

A concrete memory/traceability proposal for long-running agents (from the OpenClaw ecosystem)

A design note shared in the OpenClaw orbit argues that between “the filesystem and the token window,” agents need an operational layer that selects/compresses context per turn and emits a manifest recording what was included/excluded and why @koylanai. The same thread proposes treating “everything as a file,” with a structured namespace for logs, episodic memory, durable facts, scratchpads, tools, and sessions—so context assembly becomes debuggable and memory changes become auditable@koylanai.

Why it matters: As token windows reset and sessions extend, systems that can answer “what did the agent load and what did it skip?” become operationally important—not just nice-to-have debugging tools @koylanai.

Anthropic expands in India (office + partnerships) amid highly technical usage

Bengaluru becomes a regional base; India is Claude’s #2 market

Anthropic announced it’s opening a Bengaluru office as its home base in India and its second office in Asia-Pacific@AnthropicAI. The company said India is its second-largest market for Claude and it’s launching partnerships to deepen its commitment @AnthropicAI.

Why it matters: This is a clear “on-the-ground” expansion signal in a market Anthropic is explicitly prioritizing @AnthropicAI.

Keynote metrics: revenue acceleration, enterprise training, and Indic language support

In an Anthropic Builder Summit keynote, the event stated India is the second largest user base for Claude globally, with run-rate revenue almost doubled since an October operations announcement, and that 6% of overall conversations come from India moneycontrol. The keynote also claimed 10 Indic languages have been added to Claude in the last six months moneycontrol and highlighted partnerships/training—e.g., a public-private effort where about 500,000 employees are being trained on Claude, alongside partnerships including Accenture, IBM, Cognizant, and Deloitte moneycontrol.

Why it matters: Anthropic’s India push is framed less as consumer growth and more as developer + enterprise deployment intensitymoneycontrol.

Benchmarks and research: testing what “agentic” and “reasoning” really mean

AIRS-BENCH: 20 agent tasks sourced from 17 ML papers

Import AI summarized AIRS-BENCH, which evaluates agents across 20 distinct tasks drawn from 17 recent ML papers, spanning areas like molecules/proteins, QA, time series, text classification, code, and math Import AI. Examples include generating multiple Python solutions per problem and time series forecasting on rideshare data Import AI.

Why it matters: The benchmark is explicitly about agents doing multi-step work across task types, not just single-shot Q&A Import AI.

“First Proof”: unpublished frontier math questions with encrypted answers

Import AI also covered First Proof, a set of ten math questions (from fields including algebraic combinatorics, algebraic topology, stochastic analysis, and more) where answers are known to the authors but kept encrypted briefly Import AI. The authors claim current top public systems “struggle” to solve many questions one-shot; Import AI notes GPT 5.2 Pro and Gemini 3.0 DeepThink did not solve it in their tests Import AI.

Why it matters: Keeping solutions off the open internet is an attempt to reduce contamination and test “frontier” problem-solving under cleaner conditions Import AI.

A caution on “reasoning gains”: are they confounded by corpus expansion?

A newly discussed paper asks how much apparent reasoning improvement is confounded by expanding training corpora 10,000×, and how much performance reflects “local” generalization via pattern-matching to semantically equivalent training data @g_leech_. Nathan Lambert highlighted it as work enabled by open efforts like OLMo @natolambert.

Why it matters: If true, it raises the stakes for evaluations that can separate durable reasoning from training-data adjacency effects @g_leech_.

AI-assisted physics: “gluon amplitudes” preprint backstory

Greg Brockman said a recent preprint on gluon amplitudes sparked discussion and that AI helped crack a problem that had stumped the team for a year @ALupsasca.

Why it matters: This is another data point for AI being used as a contributor inside technical research workflows—alongside ongoing debate about how to measure that reliably @ALupsasca.

Software engineering: “agent-first” pressures on languages, dependencies, and verification

Expert theses: monoliths return, the Lindy effect weakens, and types may matter more

Thomas Wolf argued that if rewriting and understanding large codebases becomes cheap, incentives to rely on deep dependency trees collapse—bringing a “return of monoliths” and a smaller supply-chain attack surface @Thom_Wolf. He also suggested the Lindy effect weakens (legacy code is easier to explore and replace), while emphasizing that unknown unknowns remain—and formal verification becomes essential in an AI-dominated world @Thom_Wolf.

Why it matters: This frames a plausible shift from “library ecosystems” to rebuild/verify pipelines—with security and correctness as first-class constraints @Thom_Wolf.

Karpathy + Carmack + Mojo: translation is the sweet spot, but speedup claims come with caveats

Andrej Karpathy argued LLMs are especially good at translation versus de-novo generation because the original codebase acts as a detailed prompt and a reference for tests; he also questioned whether Rust is optimal as a target language for LLMs @karpathy. In a related exchange, Chris Lattner said Mojo is being built as such a target language and claimed people are “one-shotting” large Python-to-Mojo conversions and seeing 1000× speedups@clattner_llvm. John Carmack responded that 1000× claims are “normally” unreliable, but starting from Python can make them possible @ID_AA_Carmack.

Why it matters: “Rewrite everything” becomes more credible if translation is robust—but the bar for trustworthy speed/correctness claims will rise alongside it @karpathy +1.

Autonomy in the real world: Waymo scales rides; Tesla tees up a steering-wheel-free vehicle

Waymo: sixth-gen platform enters “full autonomous operations”

A post said Waymo is starting “full autonomous operations” with its 6th gen platform@reed. Another post attributed to François Chollet reported Waymo does 500,000+ driverless rides per week and is “growing at 3× per year,” with the 6th gen platform reportedly costing ~$70k per vehicle (with potential to fall by 50% over two years) @fchollet.

Why it matters: The combination of ride volume and hardware cost trajectories points to autonomy progressing as an operational scale story, not only a model capability story @fchollet.

Tesla: Cybercab (no pedals, no steering wheel) “starts production in April”

Elon Musk wrote that Cybercab, described as having no pedals or steering wheel, starts production in April@elonmusk. A separate post noted Musk predicted “10 years ago” that cars would later have no steering wheels, and the audience laughed at the time @JonErlichman.

Why it matters: If production begins as stated, it’s a notable milestone for consumer-facing autonomy-focused vehicle design (at least in form factor) @elonmusk.

Societal and policy pressure: labor/capital splits, content backlash, and “timing AGI” arguments

Bengio: longer-horizon planning is growing fast; economic transition risk centers on capital capture

Yoshua Bengio said the “duration of the tasks” AIs can do is “doubling every seven months,” and suggested that if the curve continues, systems could reach “human level” in about five yearsSilicon Valley Girl. He also warned that as more jobs become doable by machines, gains may flow to “capital” (owners of machines), putting “the vast majority of workers” in trouble, and said governments haven’t thought carefully enough about the transition Silicon Valley Girl.

Why it matters: This pairs a concrete capability-growth claim with a distributional warning—an increasingly common framing as agentic automation moves from prototypes to workflows Silicon Valley Girl.

Khosla: tax policy tweaks as labor share declines

Vinod Khosla argued AI will transform economies and that labor’s portion of the economy (vs. capital) will “decline sharply,” suggesting equalizing capital gains tax with ordinary income and noting that 40% of capital gains taxes are paid by those with income >$10M/year@vkhosla. In a follow-up, he suggested equalizing capital gains tax and removing breaks could “eliminate bottom 125 million taxpayers from the tax rolls” while staying revenue neutral, and asked economists to check the math @vkhosla.

Why it matters: This is an example of AI-driven automation debates translating into specific—and contested—tax policy proposals @vkhosla +1.

ChinAI: public backlash to AI-generated culture content in China (#反ai)

ChinAI reported rising public resistance to AI-generated content in China, citing the #反ai hashtag on Xiaohongshu reaching 5.1M views and 40,000 discussion threads by January 2026 ChinAI Newsletter. It also described platform-level pressure: Tomato Novel saw 5,600+ new books through March 1, 2024 (vs. 400 in the same period in 2023), and Ximalaya’s AI-generated content rate reportedly hit 30% by April 2025 ChinAI Newsletter.

Why it matters: The “who wants this?” question is becoming visible in consumer platforms—not just in labor markets—through measurable backlash and moderation strain ChinAI Newsletter.

Bostrom: “swift to harbor, slow to berth” on AGI timing

Import AI highlighted Nick Bostrom’s view that the choice isn’t between a risk-free baseline and a risky AI venture, but between “different risky trajectories,” and that delays can increase suffering Import AI. He summarized his position as “swift to harbor, slow to berth”: move quickly toward capability, then potentially slow down during critical stages of scaleup and deployment Import AI.

Why it matters: The argument captures a live tension in the field: how to weigh near-term benefits against tail risks as capability and deployment speed increase Import AI.

Big Ideas

1) AI is shifting the PM job market from “nice-to-have” to table stakes

AI is now explicitly part of a large (and growing) share of PM hiring: 27% of open PM roles on LinkedIn mention AI (11,832 of 44,453), up from 20% last year, 10% the year before, and 2% earlier Aakash Gupta. Aakash Gupta frames this as a widening gap between PMs who have been building agents/running evals for years and late adopters Aakash Gupta.

Why it matters: “AI PM” is increasingly becoming PM, and companies are asking candidates and teams, “How well are you using and building AI?” Aakash Gupta.

How to apply: treat your AI skill-building like core craft development—ship something, learn the vocabulary, and build repeatable practices (e.g., evals, orchestration) rather than one-off prompt work Aakash Gupta.

2) A practical strategy model: three “juggling acts” (and how teams drift into crisis)

The Beautiful Mess offers a simple diagnostic: teams often operate in strategic juggling, lazy juggling, or survival jugglingThe Beautiful Mess.

• Strategic juggling preserves optionality intentionally, with explicit tradeoffs and periodic pruning/rebalancing The Beautiful Mess.
• Lazy juggling is novelty/anxiety-driven work without prioritization discipline or measurement The Beautiful Mess.
• Survival juggling is overload imposed by reality—tradeoffs are forced, and dropping anything has immediate consequences The Beautiful Mess.

Why it matters: teams can slip from strategic → lazy when pruning and learning loops break down The Beautiful Mess, and from lazy → survival when drift and deadlines converge The Beautiful Mess. Escaping survival mode requires painful tradeoffs and stabilization first The Beautiful Mess.

How to apply: use the assessment questions (e.g., “Which balls are true optionality vs fear-driven?”) to force an explicit reset conversation before reality forces one The Beautiful Mess.

3) If AI commoditizes product building, distribution + durable moats matter more

Lenny Rachitsky argues that as AI absorbs more of product building (from code completion through deciding what to build), the biggest human challenge/opportunity becomes distribution—getting attention in an ever-louder market@lennysan. He adds this dynamic tends to benefit incumbents/platforms, making it harder for startups to break through @lennysan.

Ryan Hoover similarly pushes back on “speed is the moat,” saying it’s “comical” when feature replication takes hours @rrhoover. He points to “real moats” like network effects, proprietary data, and regulatory licenses becoming more important @rrhoover.

Why it matters: when building gets cheaper/faster, differentiation shifts toward who can reach users (or embed into workflows) and who has structural defensibility.

How to apply: when evaluating bets, explicitly separate (1) build advantage vs (2) distribution advantage vs (3) defensibility—and don’t let “we’ll move fast” stand in for moat thinking @rrhoover.

4) “Scaling yourself” as a product leader is about support systems + ruthless delegation

A Teresa Torres episode observes product leaders often struggle to scale their impact by building a support network—e.g., it’s “super rare” for CPOs/Heads of Product to have executive assistants compared with CEO/CFO peers All Things Product with Teresa …. The core framing: focus on what you uniquely do, and delegate the rest to avoid calendar overload All Things Product with Teresa ….

Why it matters: senior scope expands faster than individual capacity; without support, leaders drift into survival juggling.

How to apply: consider lightweight help (even 3–6 hours/week) for calendar/email triage and boundary enforcement All Things Product with Teresa …, plus data/research support for strategy discovery work All Things Product with Teresa ….

5) Context reconstruction is becoming a first-class tax on PM execution

A solo PM describes reopening a long list of tools daily (Notion/Jira/Slack/GitHub/Linear/docs/customer notes) and spending ~30 minutes reconstructing context before doing useful work r/prodmgmt. The thread highlights that the energy drain is often rebuilding the mental model, not the work itself r/prodmgmt.

Why it matters: scattered context makes execution slower and increases decision churn (re-litigating “why did we do this?”).

How to apply: treat “fast access to decision context” as an operational requirement, not a personal productivity hack (see playbook below) r/prodmgmt.

Tactical Playbook

1) Dialectical Bootstrapping for better estimates (and deeper AI-assisted reasoning)

Dialectical Bootstrapping is described as a systematic “prompt” to think through plausible alternatives and then provide a second guess, improving estimates from stakeholders/devs r/ProductManagement.

Steps

1. Ask for a first estimate/plan.
2. Force at least 2–3 plausible alternatives (different approaches, constraints, or hidden dependencies) r/ProductManagement.
3. Re-estimate with those alternatives in mind (the “second guess”) r/ProductManagement.
4. Capture the assumptions behind the estimate so the team can revisit them when reality changes.

Why it matters: it makes “what could be true instead?” a default step—useful with humans and when prompting agents.

2) A roadmap-safe way to handle “urgent” stakeholder change requests

A product-specialist interview scenario: a stakeholder change request would reduce manual processes and is perceived as urgent for service standards, but it conflicts with a 2-month prioritized roadmap with aligned backlogs r/prodmgmt.

Steps

1. Validate the request (what problem, for whom, and what makes it urgent?) r/prodmgmt.
2. Align on requirements with the team (what exactly must change; what’s in/out?) r/prodmgmt.
3. Prioritize against existing commitments (explicit tradeoff vs the current roadmap) r/prodmgmt.
4. Make assumptions explicit (e.g., operational impact, service-standard risk, adoption) r/prodmgmt.
5. List other considerations that could change sequencing (dependencies, risk, capacity, governance) r/prodmgmt.

Why it matters: it converts “urgent” into a structured decision instead of a bypass around prioritization.

3) Don’t let churn become a Rorschach test—unpack what it represents

A Reddit post argues that not all early churn signals product failure; it can reflect poor segment fit, exploratory users, or acquisition misalignment r/prodmgmt. The same churn number can trigger very different interpretations across a team r/prodmgmt.

Steps

1. Before labeling churn as failure, test which bucket it’s in: segment fit vs exploratory behavior vs acquisition mismatch r/prodmgmt.
2. Surface competing interpretations explicitly (what story is each function telling with the same number?) r/prodmgmt.
3. Decide what additional evidence would disconfirm each story (so you’re not overinterpreting) r/prodmgmt.

Why it matters: churn is an outcome metric; without context, teams can “fight the narrative” instead of fixing the underlying problem.

4) Capture spontaneous feedback without attracting discount spam

A PM asked for tools that help gather spontaneous feedback after discount-based incentives produced spam/fake answers r/ProductManagement. Suggestions included:

Option A: On-page micro-surveys (Hotjar)

1. Add an on-page survey widget.
2. Ask users to rate the experience, then optionally provide details (a slide-up window in the bottom-right was cited) r/ProductManagement.

Option B: Lightweight feedback ops (modu.io)

• Use modu.io for feedback boards + roadmaps, surveys, changelogs, and ratings r/ProductManagement.

Why it matters: “in-the-moment” feedback can reduce incentive-driven gaming.

5) Delegation that actually sticks: the “80% rule” + mixed AI/human support

The Teresa Torres conversation offers a delegation mindset: “80% done by someone else is 100% awesome”—a way to overcome perfectionism and offload the first 80% of work All Things Product with Teresa …. It also notes leaders may think “AI can do it,” but a human assistant can still help by taking work off your plate (including delegating some AI workflow building) All Things Product with Teresa ….

Steps

1. Identify what value you uniquely bring, and protect time for it All Things Product with Teresa ….
2. Delegate work that others can do—even if it comes back at ~80% quality All Things Product with Teresa ….
3. Use support to enforce boundaries (e.g., an EA saying “no” on your behalf) All Things Product with Teresa ….
4. Learn delegation patterns from peers/mentors (not just trial-and-error) All Things Product with Teresa ….

Case Studies & Lessons

1) Shopify operationalizes “AI-first” in performance reviews and staffing

Shopify reportedly includes AI usage in performance reviews, requires teams to prove AI can’t do the job before requesting headcount, and has cut its workforce by about a third since 2022 while selectively investing in AI engineers at higher compensation Aakash Gupta.

PM takeaway: incentives and operating mechanisms (reviews, headcount gates) can rapidly change day-to-day behavior—faster than “AI strategy” decks.

2) When “disciplined optionality” becomes exhaustion: a multi-bet strategy failure mode

A case describes leadership launching several major bets to hedge uncertainty; in practice, teams felt stretched, the core product slipped, and none of the initiatives achieved decisive traction The Beautiful Mess. The diagnosis hinged on an unproven assumption that one bet would eventually break out—yet the organization was too tired from juggling to run disciplined learning loops The Beautiful Mess.

PM takeaway: optionality only stays strategic if you maintain pruning and learning mechanisms; otherwise it drifts toward “wait and see” and burnout The Beautiful Mess.

3) Buildathon evidence: PMs can ship real agents quickly—with real product thinking

In The Product Compass’s AI Agents Buildathon for PMs, 36 teams shipped working AI agents (not mockups), including value propositions, differentiation, defensible moats, and designed guardrails/autonomy boundaries The Product Compass. The post encourages studying how teams set autonomy boundaries and handled tradeoffs and real-world challenges The Product Compass.

PM takeaway: the bar is moving from “AI ideas” to “AI systems with constraints, evals, and clear value.”

4) From information to execution: OpenClaw as an agent-driven operations layer

A session on OpenClaw describes using an AI assistant to scan meeting transcripts/documents and Asana tasks to identify what was missed and auto-add items to the Asana board, framing the shift as “information to execution” Product Management Exercises. It also calls out practical constraints: no monthly SaaS fees, but LLM usage can get expensive (e.g., $200 in two days), prompting a cost strategy using different models for different tasks Product Management Exercises.

PM takeaway: “agentic” workflows get real when they write back to systems (tasks, calendars) and when you design for cost/control upfront.

5) Solo PM reality: PRDs are easy; rebuilding context is the work

The solo-PM thread summarizes the pain succinctly: “Writing the actual PRD is easy. Rebuilding the context behind it is the real work.” r/prodmgmt

PM takeaway: invest in whatever reduces “context fetch time,” or you’ll spend your best hours re-deriving decisions.

Career Corner

1) The AI PM skill stack is becoming a distinct vocabulary (and hiring filter)

Aakash Gupta highlights that AI-related PM roles increasingly require vocabulary such as context engineering, orchestration, observability, evals, and that many PMs still struggle to explain the difference between RAG vs. fine-tuningAakash Gupta. He also notes traditional companies cutting PMs (e.g., FedEx, UPS, Target) while AI leaders (OpenAI, Google) and AI arms hire them Aakash Gupta.

How to act this quarter:

• Prioritize one “proof of work” project that forces you to use evals/agent constraints in practice.
• Benchmark your skills against job market reality (27% already AI-tagged) Aakash Gupta.

2) “Ship” beats “study” for confidence with technical counterparts

A testimonial from a Director of Automation describes discomfort engaging engineers/architects on GenAI due to lack of understanding, and says confidence improved dramatically after a cohort that introduced concepts and required applying them by the end Product Management Exercises.

How to apply: choose learning formats that end with building something (even a small prototype) so you can ask better questions and evaluate where an ML solution fits Product Management Exercises.

3) Advancing as a leader: build a support system (and use AI + humans together)

The Teresa Torres episode recommends considering:

• An executive assistant (even part-time) to manage calendar/email and enforce boundaries All Things Product with Teresa …
• A researcher/data person for strategy discovery and KPI analysis All Things Product with Teresa …
• Coaches for clarity, public speaking, leadership, and accountability All Things Product with Teresa …
• A community of practice for onboarding, candidate pre-qualification, and role descriptions All Things Product with Teresa …

Tools & Resources

• AI PM market + skills roundup (Aakash Gupta)Aakash Gupta

  • Context engineering: https://www.news.aakashg.com/p/rag-vs-fine-tuning-vs-prompt-engineeringAakash Gupta
  • AI evals: https://www.news.aakashg.com/p/ai-evalsAakash Gupta
  • AI agents: https://www.news.aakashg.com/p/ai-agents-pmsAakash Gupta
  • How to become an AI PM: https://www.news.aakashg.com/p/how-to-become-and-succeed-as-an-aiAakash Gupta

• AI Agents Buildathon solution gallery (The Product Compass): https://www.productcompass.pm/p/ai-solutions-galleryThe Product Compass

  • Intent engineering framework: https://www.productcompass.pm/p/intent-engineering-framework-for-ai-agentsThe Product Compass
  • Context engineering guide: https://www.productcompass.pm/p/context-engineeringThe Product Compass
  • AI product strategy (moats/costs/distribution): https://www.productcompass.pm/p/openai-how-to-build-ai-product-strategyThe Product Compass
  • Evaluating AI agents (evals): https://www.productcompass.pm/p/how-to-evaluate-ai-agents-n8nThe Product Compass

• Support systems for product leaders (YouTube): https://www.youtube.com/watch?v=ayKoE1MvxpMAll Things Product with Teresa …

• OpenClaw for product managers (YouTube): https://www.youtube.com/watch?v=gINPJWwFVLsProduct Management Exercises

• Spontaneous feedback collection (thread + tools)

  • Hotjar on-page surveys (slide-up rating + details) r/ProductManagement
  • modu.io: http://modu.ior/ProductManagement

• Threads worth scanning

  • Dialectical Bootstrapping (estimation + deeper thinking): https://www.reddit.com/r/ProductManagement/comments/1r6plne/r/ProductManagement
  • Prioritizing change requests (interview scenario): https://www.reddit.com/r/prodmgmt/comments/1r6rahx/r/prodmgmt
  • Early churn nuance: https://www.reddit.com/r/prodmgmt/comments/1r6fj1j/r/prodmgmt
  • Solo PM context rebuilding: https://www.reddit.com/r/prodmgmt/comments/1r6nen9/r/prodmgmt

Most compelling recommendation (strongest endorsement)

“Software moats in the AI era” (X post/article)

• Title: Software moats in the AI era (as described by the recommender) @patrick_oshag
• Content type: X post / X article @patrick_oshag +1
• Author/creator: @nicbstme @patrick_oshag
• Link/URL:
  • Post: https://x.com/nicbstme/status/2023501562480644501@patrick_oshag
  • Article: http://x.com/i/article/2023495526877155328@nicbstme
• Who recommended it: Patrick O’Shaughnessy (@patrick_oshag) @patrick_oshag
• Key takeaway (as shared): O’Shaughnessy calls it “The best post I’ve read on software moats in the AI era”@patrick_oshag.
• Why it matters: It’s a high-conviction pointer to a single piece on how defensibility/moats may be changing under AI, from an investor explicitly ranking it above other reads in the same category @patrick_oshag.

Two more high-signal reads (strategy + valuation)

CAP (PDF): durability and “terminal value” in valuations

• Title: CAP (PDF) @bgurley
• Content type: PDF @bgurley
• Author/creator: Not specified in the source excerpt @bgurley
• Link/URL: https://pages.stern.nyu.edu/~adamodar/pdfiles/eqnotes/cap.pdf@bgurley
• Who recommended it: Bill Gurley (@bgurley) @bgurley
• Key takeaway (as shared): Gurley frames it as a lens for valuation in a world where “so much value is in terminal value,” raising the question: “will this company be around in 30 years?”—and notes this is an “easy question for Walmart,” in the context of comparing Walmart/Costco multiples vs. software companies @bgurley. He also adds: “AI causes CAP anxiety.”@bgurley
• Why it matters: It’s a direct pointer to thinking about business durability when long-duration assumptions dominate valuation—and how AI may be pressuring those assumptions @bgurley +1.

The Cathedral and the Bazaar (essay/paper): open source as a competitive weapon

• Title: The Cathedral and the Bazaar @bgurley
• Content type: Essay/paper (PDF) @bgurley
• Author/creator: Not specified in the source excerpt @bgurley
• Link/URL: https://users.ece.utexas.edu/~perry/education/382v-s08/papers/raymond.pdf@bgurley
• Who recommended it: Bill Gurley (@bgurley) @bgurley
• Key takeaway (as shared): Gurley argues people “don’t understand what a competitive strategic weapon open source has become and how it works,” and says it’s “always good to reread the cathedral and bazaar” @bgurley.
• Why it matters: A reminder that open source can be a strategic instrument, not just a development model—worth revisiting if you’re thinking about competition in software markets @bgurley.

Market Movers

Soybeans & grains (U.S. / global)

• Soybean positioning and demand signals: With U.S. grain and soybean trading closed for Presidents Day, one market summary noted that investors raised bullish bets on soybeans@SuccessfulFarm. Separately, a commodity outlook tied recent soybean price strength to U.S.–China shipments gaining pace, alongside attention to potential La Niña-induced drought risks for U.S. farmers going into springThe Commodity Report.
• Trade headline risk: In a policy interview, the host referenced President Trump saying China committed to buying an additional 8 million metric tons of soybeans this marketing year; the interviewee framed it as trust but verify and emphasized the need for enforceable trade mechanisms Brownfield Ag News.
• Storage and supply backdrop: A separate D.C. policy discussion noted higher-than-normal grain balances after large U.S. wheat/soy/corn harvests, though much of the grain on the ground has been moving through the channel Brownfield Ag News.

Longer-cycle pricing

• A Successful Farming post linked to an expert view that corn and soybean prices are in the trough of the current cycle, with the possibility they begin to rise in 2027@SuccessfulFarm.

Livestock

• A cattle industry outlook cited low cow inventory and resilient beef demand as supporting near- to mid-term strength Farm4Profit Podcast. A separate commodity note added that the fundamental case for higher cattle prices remains intact and that herd rebuilding can take a decadeThe Commodity Report.

Agricultural inflation (Turkey)

• Turkey’s Agricultural Producer Price Index (Tarım-ÜFE) rose 8.46% month-over-month and 43.58% year-over-year in January; the sharpest monthly increases cited were 30.43% (vegetables/melons/roots & tubers) and 30.11% (citrus) @irfandonat.

Innovation Spotlight

Precision application + automation on a North Dakota operation (U.S.)

A producer in west central North Dakota described measurable outcomes from John Deere automation and targeted-application tools:

• See & Spray: Reported a 96% chemical savings in a low-weed field, plus improved visibility into where weeds persist through weed-density mapping RDO Equipment Co.. The same operator highlighted the learning curve around selecting spray tips and trusting the system RDO Equipment Co..
• ExactShot fertilizer placement: Reported using 0.8 gallons/acre of 10-34-0 on corn (equated to five gallons/acre), with no yield drop versus prior practice while retaining fertilizer savings RDO Equipment Co..
• Harvest automation: Automation revealed the cost of pushing late—estimated 0.5 to 1 bushel/acre thrown over in lower-light / changing-wind conditions—leading to earlier shutdown decisions RDO Equipment Co..

Organo-mineral and “special fertilizers” (Brazil)

• Brazil’s “special fertilizers” segment (including organo-minerals) was described as having grown ~19% in 2024, with continued expansion expected into 2026Canal Rural.
• In Santa Catarina, Fecoagro (11 cooperatives) was highlighted using organo-minerals, including inputs derived from leather waste processed via hydrolysis to produce amino acids and organic nitrogen incorporated into fertilizer formulations Canal Rural.
• The approach was framed as integration with conventional fertilizers (not replacement) to improve nutrient-use efficiency and reduce losses, including via slower nutrient release Canal Rural.

Coffee climate-risk and compliance tooling (Brazil / EU-linked)

• Minas Gerais coffee stakeholders were offered a platform from the University of Prague integrating satellite data and geospatial information to help assess water deficit, insolation, and vulnerable areas for climate-impact mitigation Canal Rural.
• The Comunidade platform (already applied in Colombia and Chile) was presented as supporting field decisions such as water management and climate-risk mitigationCanal Rural. Minas Gerais also cited its Selo Verde MG platform to expand traceability and support EU export complianceCanal Rural.

Livestock analytics for real-time breakevens (U.S. / livestock)

• Performance Livestock Analytics described its Performance Beef platform starting with iPad-based feed tracking to capture every pound loaded and delivered to bunks Farm4Profit Podcast.
• A chute-side module using UHF tags was described as enabling hands-free capture of nutrition, health, and financial data and providing real-time breakevensFarm4Profit Podcast.
• A related “auction advisor” tool was described as calculating breakevens in real time to avoid overbidding at auction Farm4Profit Podcast.

Regional Developments

Brazil: Mato Grosso harvest delays and safrinha risk

• Excessive rains in Mato Grosso were reported to be delaying soybean harvest and pushing second-crop corn (safrinha) planting outside the safer window Canal Rural.
• In Paranatinga, early corn plantings were described as flooded (plants failing to emerge/develop after six days of rain), and planned safrinha area was expected to be reduced from 300–320 hectares, implying lower production Canal Rural. A nearby property cited 1,800 mm accumulated rainfall versus a 2,200–2,400 mm historical range Canal Rural.
• Weather coverage also described only a short window to accelerate soybean harvest and corn planting before heavier rains return (volumes cited >280 mm starting around Feb. 20–21) Canal Rural.

Brazil: pests, costs, and fitosanitary pressure in soy

• In Mato Grosso, soybean production costs were cited at R$6,000–7,000 per hectare, with ~30% concentrated in agricultural defensives Canal Rural.
• A highlighted emerging concern was mosca-da-larva-minadora, described as reducing leaf area, shortening the cycle, and compromising grain fill; the damage was also linked to opportunistic diseases such as mancha-alvo and cercosporoseCanal Rural.
• Soil nematodes were described as major yield threats; estimates cited R$36 billion in damages across crops per season and ~R$16 billion for soy alone, with Pratylenchus emphasized in Mato Grosso soils Canal Rural.

Brazil: export performance and market diversification

• Brazil’s agribusiness exports totaled US$10.8B in January 2026, down 2.2% YoY despite +7% volume, attributed to an 8.6% decline in average prices amid lower international commodity prices Canal Rural +1.
• China (20%), the EU (11%), and the U.S. (6.6%) were cited as top buyers; exports to ASEAN grew 5.7% in January 2026 vs. a year earlier Canal Rural. Beef exports were highlighted at US$1.3B and 231k tons shipped to 116 countries, with U.S. purchases up 93% in January Canal Rural.
• Coverage of Brazil’s upcoming mission to India and South Korea framed the objective as diversifying partners; India exports were cited at ~US$7B in 2025 (including sugar and soy oil) with a stated target of US$20B in coming years Canal Rural.

U.S.: processing capacity and trade-policy calendar

• Smithfield Foods was reported to be planning a new processing facility in Sioux Falls, South Dakotar/farming.
• A U.S. policy interview noted the USMCA is entering a formal review process, with a joint review expected to begin July 1; the interviewee argued the agreement has been “fantastic” for U.S. agriculture and emphasized extending it for the full 16 years, with dairy cited as an area to improve Brownfield Ag News.

Best Practices

When corn yields are strong but soybeans disappoint (field diagnostics)

A practical checklist for “great corn, poor soybeans” emphasized starting with:

• Iron deficiency chlorosis; insects (e.g., gall midge larvae); soybean cyst nematodes; diseases (white mold, pythium, sudden death syndrome); planting timing; and weed control @AgPhDMedia.
• The most common drivers cited were poor drainage and potassium. Soybeans were described as having one-fifth the root mass of corn, making them more sensitive to drainage issues, and as needing more than double the potassium per day (at peak) compared with corn @AgPhDMedia.
• A field-proven fix cited was installing tile and applying substantial potassium, which was said to have nearly doubled soybean yields a decade earlier @AgPhDMedia.

Broiler pre-slaughter handling (Brazil)

Operational steps highlighted to reduce condemnations and protect carcass quality:

• Fasting: Follow integrator timing with partial feed-cut so total fasting does not exceed 12 hours; water remains available Canal Rural.
• Movement + water access: Gentle movement plus adequate drinker flow to support excretion; mishandling was linked to stress, piling, scratches, dermatitis, and issues such as “full rectum” at the plant Canal Rural.
• Loading: Box birds by weight (example given: 6–9 birds per box) and prioritize cooler hours; post-loading truck wetting (“molha-frango”) was described as helping cooling and welfare in transit Canal Rural.

Pasture brush control (U.S.)

• Options cited for controlling woody brush included burning, herbicides, and using goats@SuccessfulFarm.

Swine carcass composting (Brazil; Embrapa method)

• A method described layering 60 cm substrate, placing a carcass (with perforation in the abdomen to prevent bloating), covering with another 60 cm substrate, and humidifying only in very dry regions; it was described as avoiding odor/flies/contamination via substrate absorption Canal Rural.

Input Markets

Fertilizer and crop protection

• Brazil (soy): Field cost estimates of R$6,000–7,000/ha, with ~30% in defensives, underscore the economic weight of pest/disease management Canal Rural.
• Brazil (fertilizers): Special fertilizers were cited as growing ~19% in 2024 with momentum expected through 2026Canal Rural.
• U.S. (inputs): A D.C. policy interview cited rising costs across fertilizer, seed/chemistry, land, interest rates, and agricultural labor, and noted the administration removed tariffs on fertilizer imports as relief Brownfield Ag News.

Labor

• A California specialty-crop operator cited minimum wage rising to $17, with overtime starting at 40 hours, describing labor costs as “astronomical” for labor-intensive crops Farm Journal.

Product/application selection reminders

• An Ag PhD post highlighted differences between zinc sulfate and zinc EDTA (zinc sources) @AgPhDMedia.
• Another Ag PhD clip noted “many variations” in wheat herbicides@AgPhDMedia.

Forward Outlook

• Brazil (Mato Grosso): Watch for continued harvest disruption as rain is expected to intensify again (including a short harvest/planting window before heavier totals cited around Feb. 20–21) Canal Rural. More broadly, Canal Rural weather coverage flagged that rains could tighten again Feb. 22–26, with soybean harvest areas already experiencing saturated soils Canal Rural.
• Row-crop planning (U.S.): Spring crop-mix decisions were described as often coming late; one policy interview suggested farmers are watching South American supply and markets to choose the right corn/soy mix Brownfield Ag News.
• Price-cycle context: A cited view places corn/soy prices in a cyclical trough, with a possible upswing not expected until 2027@SuccessfulFarm.
• Trade calendar (North America): The USMCA joint review process is expected to start July 1, with calls to extend the agreement for the full 16 yearsBrownfield Ag News.
• Commodities allocation theme (2026): One commodity note expects 2026 returns to be driven largely by agri-commodities rather than metals The Commodity Report.

🔥 TOP SIGNAL

A practical pattern is crystallizing for long-running coding agents: treat context + memory as a filesystem the agent can browse on-demand, rather than a one-time prompt dump. @koylanai lays out a concrete design (namespaced /context/*, runtime list/read/search, per-turn manifests of what was loaded/skipped, and a scratchpad→episodic→fact memory lifecycle) that specifically targets compaction loss and “silent” memory failures—and claims it’s been validated in production via Cursor’s file-based “dynamic context discovery.” @koylanai

🛠️ TOOLS & MODELS

• Showboat ecosystem expands (Simon Willison)

  • Showboat v0.6.0 “remote”: set SHOWBOAT_REMOTE_URL and every showboat init/note/exec/image POSTs document fragments to your endpoint for live viewing while the agent works. simonwillison
  • New companion tools:
    • Chartroom: CLI charting (matplotlib wrapper) designed to generate charts that can be embedded in Showboat docs. simonwillison
    • datasette-showboat: Datasette plugin with /-/showboat viewer + /-/showboat/receive endpoint to receive streamed updates. simonwillison
  • Details: https://simonwillison.net/2026/Feb/17/chartroom-and-datasette-showboat/@simonw

• Claude Code for web + Desktop workflow (Simon Willison)

  • He uses Claude Code on the web (container-managed environment) but accesses it via iPhone/Mac desktop apps. simonwillison
  • Tip: if Claude Code is looking at screenshots, you can view those screenshots yourself in the desktop chat transcript. @simonw

• Model speed/cost trade-off (Armin + Ben)

  • Claude “Fast mode”: reported as 2.5× throughput for 6× cost, and Armin says he burned the included $50 in a single session. Armin Ronacher
  • Codex 5.3: Armin says he uses 5.3 “exclusively” because it feels noticeably faster, without seeing a programming-quality jump. Armin Ronacher

• Standards & UX nits that keep coming up

  • agents.md standardization: Theo complains about having to symlink Claude.md to AgentsMD, and says OpenAI is moving “everything to AgentsMD.” Theo - t3․gg
  • Antigravity UI editing: demo suggests selecting an area on a screenshot and describing layout changes—positioning it as a faster interface than writing long text specs. @antigravity

💡 WORKFLOWS & TRICKS

• Make context assembly debuggable (filesystem + manifests)

  • Core move: put everything under a predictable /context/ namespace and give the agent runtime file ops (list/read/write/search), so it can discover what exists before loading it. @koylanai
  • Add an operational layer:
    1. Constructor: selects/compresses context into a token-budget input and emits a manifest (what was selected/excluded and why). @koylanai
    2. Updater: streams extra context during the turn, swapping pieces based on feedback instead of stuffing everything at boot. @koylanai
    3. Evaluator: checks outputs against sources, writes verified info back as structured memory, and flags human review when confidence is low. @koylanai
  • Memory lifecycle that avoids “one forever file”: split scratchpad / episodic / fact, with promotion + retention policies. @koylanai

• Non-breaking migration path for existing agents (koylanai)

  1. Start returning file references before snippets + emit per-turn load manifests. @koylanai
  2. Expose /context/* and allow runtime list/read so the agent can browse without loading everything. @koylanai
  3. Switch to minimal preload + on-demand fetch; decompose MEMORY.md. @koylanai
  4. Add promotion/archival/retention policies and audit logs for reversible state transitions. @koylanai

• Live “agent work log” you can review while it’s working (Showboat remote + Datasette)

  • Run Datasette with the plugin, then point Showboat at it:

    uvx --with datasette-showboat --prerelease=allow 
      datasette showboat.db --create 
      -s plugins.datasette-showboat.database showboat 
      -s plugins.datasette-showboat.token secret123 
      --root --secret cookie-secret-123

    simonwillison

    export SHOWBOAT_REMOTE_URL="http://127.0.0.1:8001/-/showboat/receive?token=secret123"

    simonwillison

  • Simon’s take: this is “wildly useful” for Claude Code for web—he can give feedback mid-flight based on screenshots being published live. simonwillison

• Browser/manual UI testing loop with screenshots (Rodney + Claude Desktop)

  • Prompt pattern: have the agent read uvx rodney --help, then use Rodney to click around and screenshot pages. simonwillison
  • Example commands used in a session: open a page, click a selector, screenshot to /tmp/menu.png, then Read /tmp/menu.png to analyze it—while you can also see the same screenshot in Claude Desktop. simonwillison +1

• “Zero context overhead” conditional prompting (Armin’s pi extension)

  • His go-to-bed.ts injects hidden prompts only after bedtime, so it’s “zero context overhead unless it’s late at night” (implemented with an “echo hack” due to limited bash tool hooks). @mitsuhiko

• Hands-off self-repair via SSH (steipete)

  • When ClawdHub errored, Peter says “claw” fixed it while he was at the barber: it SSH’ed into his MacBook Pro and deployed there because auth wasn’t configured on the agent’s host machine. @steipete

👤 PEOPLE TO WATCH

• @koylanai — clearest end-to-end proposal today for agent memory/context reliability: filesystem namespace + manifests + constructor/updater/evaluator + lifecycle policies. @koylanai
• Simon Willison — shipping developer-grade “agent observability” tooling (Showboat remote streaming + charting + Datasette viewer) and concrete Claude Code desktop workflows. simonwillison +1
• Armin Ronacher — practical extensions that modify agent behavior with minimal context cost (bedtime injection) and experiments in inter-session orchestration. @mitsuhiko +1
• @steipete (Peter Steinberger) — rare, concrete demo of a personal agent doing remote deploy/self-repair via SSH. @steipete
• Andrej Karpathy — high-signal framing: LLMs are especially strong at translation because the existing codebase becomes a detailed prompt + test reference. @karpathy

🎬 WATCH & LISTEN

• Fast mode reality check (State of Agentic Coding #3, ~37:39–39:36)
  • Hook: what “2.5× faster” actually means in practice when it costs 6×, plus the “burned $50 in one session” datapoint. Armin Ronacher

• Build a transcript-extractor skill with an external API (Riley Brown, ~10:55–22:09)
  • Hook: end-to-end skill creation + testing loop using Supadata—then using the transcript to generate summaries and drop them into Notion (including a full transcript). Riley Brown

📊 PROJECTS & REPOS

• Showboat family (Simon Willison): Showboat, Rodney, Chartroom, datasette-showboat (linked together as a loose-conventions ecosystem). simonwillison
• Rodney --help designed for agents: https://github.com/simonw/rodney/blob/main/help.txtsimonwillison
• Armin Ronacher’s pi extensions
  • go-to-bed.ts: https://github.com/mitsuhiko/agent-stuff/blob/main/pi-extensions/go-to-bed.ts@mitsuhiko
  • control.ts (messaging/orchestration experiments): https://github.com/mitsuhiko/agent-stuff/blob/main/pi-extensions/control.ts@mitsuhiko

— Editorial take: Today’s theme is agent observability: manifests, streamed work logs, and screenshot-based feedback loops are becoming the difference between “cool demo” and “debuggable daily driver.”

Top Stories

1) Qwen3.5’s open-weight release becomes a cross-stack default (model + inference tooling + hosted endpoints)

Why it matters: A strong open-weight model only reshapes the landscape when it’s easy to run (local + cloud) and supported by the major inference stacks.

Alibaba released Qwen3.5-397B-A17B, the first open-weight model in the Qwen 3.5 series, positioned as a native multimodal model trained for real-world agents and licensed under Apache 2.0@Alibaba_Qwen. Multiple posts emphasize the architecture: hybrid linear attention + sparse MoE with 397B total parameters / 17B active, targeting throughput and latency @vllm_project.

Distribution and “day-0” enablement showed up quickly:

• Inference stacks: day-0 support in vLLM (with deployment recipes) @vllm_project and SGLang (with cookbook + PR) @lmsysorg.
• Hardware ecosystems: AMD published day-0 guidance for Instinct GPUs via SGLang/vLLM @AIatAMD. NVIDIA highlighted a free build surface and a NeMo fine-tune config @NVIDIAAIDev.
• Hosted endpoints: Together AI listed it as production-ready with a 99.9% SLA and highlighted 87.8% MMLU-Pro plus “early fusion” multimodality @togethercompute. OpenRouter also added Qwen3.5-397B-A17B and Qwen3.5 Plus variants @OpenRouterAI.
• Local options: Unsloth published local run artifacts (including a GGUF) and claimed 4-bit can run on 256GB RAM@UnslothAI. Ollama made it available on cloud with ollama run qwen3.5:cloud@ollama.

Context + efficiency were recurring themes: Qwen3.5 Plus is described as the hosted API variant of the 397B model with 1M context (vs native 256K) plus search and code interpreter @JustinLin610. Separately, an analysis of Qwen3.5’s KV-cache footprint at 262K context estimates 8.05 GB in BF16 (or 4.025 GB in FP8 KV) and attributes it to the use of 45 gated deltanet layers@bnjmn_marie.

Pricing sparked debate. Novita listed $0.6/M input and $3.6/M output tokens @novita_labs, while another post noted a China vs international API price delta (e.g., $0.12 prefill + $0.69 decode in China vs $0.4/$2.4 internationally) @sun_hanchi.

2) “Open model week” continues: MiniMax M2.5’s agentic coding positioning turns into usage share

Why it matters: Popularity signals (usage, partners, integrations) often matter as much as model evals when developers choose defaults.

MiniMax M2.5 is repeatedly framed as strong for agent workflows. MiniMax says it uses per-token process rewards to better utilize signal across reasoning steps @MiniMax_AI, and a separate post claims “frontier coding performance” at at least 1/10th the cost of closed-source models @basetenco.

Adoption signals:

• OpenRouter reported M2.5 became the most popular model and hit #1 on the weekly leaderboard in four days@MiniMax_AI +1.
• Together AI announced MiniMax M2.5 availability for production-scale agentic workflows @togethercompute +1.
• Baseten listed M2.5 on its model APIs @basetenco.
• Windsurf added GLM-5 and MiniMax M2.5 with limited-time credit discounts @windsurf.

3) Inference economics take center stage: Blackwell Ultra claims, cross-vendor benchmarking, and memory constraints

Why it matters: For agents and long-context workloads, cost-per-token and performance-per-watt increasingly set the ceiling for deployment.

NVIDIA highlighted Blackwell Ultra GB300 NVL72, claiming up to 50× higher performance per megawatt (also framed as “tokens per watt”) and 35× lower cost per token versus Hopper, aimed at low-latency and long-context agentic use cases @kimmonismus +2.

In parallel, SemiAnalysis promoted InferenceX v2 benchmarks comparing Blackwell vs AMD vs Hopper, covering systems like GB300 NVL72, MI355X, B200, H100, and techniques such as disaggregated serving and wide expert parallelism, tested across SGLang, vLLM, TRTLLM@SemiAnalysis_.

Supply-side constraints also surfaced:

• Western Digital reportedly sold out its entire 2026 hard drive capacity, with most supply locked by top enterprise customers (consumers ~5% of revenue) @kimmonismus.
• A Bloomberg-linked thread described a growing memory chip crisis, with Sony considering pushing PS6 to 2028 or 2029@tomwarren.

4) Agent reliability and efficiency become the research focal point (benchmarks + pruning + adaptive reasoning)

Why it matters: As agents move into longer-horizon workflows, the bottleneck shifts to multi-step execution quality and wasted tool calls/tokens.

Notable signals this cycle:

• WebClipper models web-agent search as state graphs and prunes into minimal DAGs, reporting ~20% reduction in tool-call rounds while maintaining or improving accuracy; it also introduces F-AE Score to balance accuracy and efficiency in trajectories @dair_ai.
• CogRouter dynamically adjusts reasoning depth step-by-step across four cognitive levels; the report cites a 7B model reaching 82.3% success on agent benchmarks while using 62% fewer tokens than a baseline it outperformed (GPT-4o is named in the post) @omarsar0.
• SciAgentGym evaluates multi-step scientific tool use with 1,780 tools across 4 disciplines; a post claims success drops from 60.6% to 30.9% as interaction steps increase, and presents SciForge (dependency-graph trajectory synthesis) with an SciAgent-8B outperforming much larger models on scientific workflows @dair_ai.

5) OpenAI adds an enterprise security posture for ChatGPT workflows

Why it matters: Prompt-injection and connected-app risks are increasingly operational issues; “security modes” change what’s viable for regulated deployments.

OpenAI introduced Lockdown Mode for ChatGPT (initially for enterprise/business users), disabling some tools/capabilities that could be exploited to exfiltrate sensitive data via attacks such as prompt injection, including switching to cached browsing and limiting broader web interaction @cryps1s.

Research & Innovation

Why it matters: The highest leverage work right now targets faster inference (one-step generation, sparse routing), better reasoning under constraints (few-parameter adaptation, RL objectives), and agent training that wastes fewer steps.

Highlights from recent papers (curated list)

• Generative Modeling via Drifting: a generative framework that evolves the pushforward distribution to enable native one-step inference, reporting FID 1.54 (latent) / 1.61 (pixel) on ImageNet 256×256 @TheAITimeline.
• TinyLoRA (“Learning to Reason in 13 Parameters”): scales low-rank adapters down to a single parameter; the post claims 91% GSM8K accuracy with 13 trained parameters and recovery of 90% of gains on AIME/MATH500 while training 1000× fewer parameters than typical LoRA approaches @TheAITimeline.
• Maximum Likelihood Reinforcement Learning: defines compute-indexed objectives interpolating RL and exact maximum likelihood; claims up to 20× test-time scaling efficiency gains over GRPO in math reasoning and code generation tasks @TheAITimeline.
• Kimi K2.5 (agentic multimodal training): combines text/vision training stages and a parallel “Agent Swarm” orchestration approach; claims 4.5× latency reduction vs single-agent baselines @TheAITimeline.
• Generative meta-models of LLM activations: trains diffusion models on 1B residual stream activations to learn priors over internal states; reported to improve fluency for steering interventions and to scale sparse probing as neurons isolate concepts @TheAITimeline.
• On-Policy Context Distillation (OPCD): trains on self-generated trajectories while minimizing reverse KL vs a context-conditioned teacher; claims improved math reasoning/text-games accuracy and stronger OOD performance, supporting cross-size distillation @TheAITimeline.
• SkillRL: builds a hierarchical skill library (SkillBank) from trajectories using retrieval + recursive evolution; claims +15.3% on ALFWorld/WebShop and reduced token footprint @TheAITimeline.
• Retrieval-aware distillation for Transformer–SSM hybrids: keeps only attention heads critical for in-context retrieval; claims retaining 2% of heads recovers 95%+ of teacher performance, enabling 5–6× memory efficiency in retrieval-heavy settings @TheAITimeline.
• ViT-5: modernizes ViT with changes to normalization/activation/gating while preserving Attention–FFN layout; claims 84.2% ImageNet-1k top-1 accuracy and FID 1.84 in an SiT diffusion framework @TheAITimeline.

Additional notable research signals

• Deep-Thinking Ratio (DTR) and Think@n: proposes a measure of “deep thinking” effort and a test-time strategy that prefers/aggregates higher-DTR generations while stopping early on low-DTR ones; claims to outperform self-consistency using ~50% less compute@WeiLin__Chen +1.
• MonoLoss: a plug-in objective for SAEs that rewards semantically consistent activations to increase monosemanticity (MonoScore) across SAEs trained on CLIP/SigLIP2/ViT features @iScienceLuvr.
• Skills as procedural knowledge: a benchmark across 86 tasks / 11 domains and 7,300+ trajectories reports curated skills improve pass rates by 16.2pp on average, while self-generated skills show no average benefit; concise skills outperform comprehensive docs @omarsar0.

Products & Launches

Why it matters: Agents and models become “real” when they’re packaged into deployable workflows: chat surfaces, developer tooling, hosted inference, and observability.

Agents in mainstream chat surfaces

• Manus Agents launched inside chat apps (starting with Telegram), offering long-term memory, multi-step execution, and tool integrations (Gmail, Calendar, Notion, etc.) @ManusAI +1. One post also claimed this helps explain Meta’s acquisition of Manus (attributed as commentary) @kimmonismus.
• SkyBot (Skywork) is positioned as a cloud-native agent for long-term task execution with zero setup (no code/keys/servers), running in the background and accessible via phone/Discord/Telegram @kimmonismus +1.

Voice and realtime interaction tooling

• NVIDIA PersonaPlex is live on fal as a full-duplex model that listens and speaks simultaneously, handles interruptions/backchannels, and aims for low-latency spoken interaction with a consistent persona @fal.

Agent harness + “background agent” infrastructure

• Ollama added subagents and web search in Claude Code, enabling parallel tasks in isolated contexts (file search, code exploration, research) and automatic web search via the Anthropic compatibility layer—no MCP servers or API keys required @ollama +1.
• Terminal Use (YC launch) provides infrastructure for background agents, including filesystem forking and parallel agent runs; YC’s framing emphasizes that agent apps often “win on the harness” rather than the model alone @ycombinator.

Developer tools and document workflows

• LlamaIndex / LlamaCloud released a parsing feature to convert complex PDFs (tables, charts, multi-column layouts) into clean markdown/JSON via clickable templates @jerryjliu0.
• Base44 launched a standalone backend (CLI-first, realtime, AI-agent friendly) for deploying auth/database/hosting from the CLI @MS_BASE44.
• Synthesia launched a Word-to-video flow (upload → adjust settings → generate video; optional brand kit/translation/interactivity) @synthesiaIO.

Industry Moves

Why it matters: Usage momentum, distribution partnerships, and supply constraints influence which models and tools become defaults.

• Codex usage momentum: OpenAI’s Sam Altman said Codex weekly users have more than tripled since the beginning of the year @sama.
• Anthropic expansion: Anthropic opened a Bengaluru office, calling India its second-largest Claude.ai market @AnthropicAI.
• Model availability as a differentiator: Artificial Analysis benchmarked Kimi K2.5 across 8 providers, showing output speed variation of ~330 tokens/s, plus differences in latency (TTFAT/TTFT), pricing, context support, and multimodality/tooling support @ArtificialAnlys.
• Autonomy in the physical world: Waymo began full autonomous operations with its 6th gen platform; one post claims Waymo does 500,000+ driverless rides/week, and cites a ~$70k per-vehicle cost with room to fall by ~50% over two years @reed +1.

Policy & Regulation

Why it matters: As deployments expand, governance shows up as procurement rules, contract terms, transparency norms, and security controls.

• Pentagon–Anthropic tension (Axios-linked): posts report the Pentagon said Anthropic will “pay a price,” while Anthropic is described as willing to loosen terms of use but seeking safeguards against mass surveillance of Americans and fully autonomous weapons @unusual_whales +1. Another Axios-linked post says the Pentagon is considering labeling Anthropic a “supply chain risk,” which could force vendors to cut ties @DavidLawler10.
• Peer review integrity: a post claims ICML journal editors inserted hidden prompt injections into papers to detect AI-assisted reviewing, causing at least one reviewer to consider desk rejection after discovering it @paul_cal.
• Labor-market signal: one post claims the US BLS revised 2025 job numbers downward by over 1 million, with the Information sector revised down 88,000 jobs (3%), attributed by economists in the post to AI automation of tech-heavy roles @kimmonismus.

Quick Takes

Why it matters: Smaller signals often become the next “normal”—or the next operational risk.

• ByteDance BitDance: an open-source autoregressive image model with a GitHub repo and details like up to 32× downsampling and a codebook size up to 2^256@aisearchio +1.
• Chinese humanoid robots (Spring Festival Gala): Unitree showed a large robot cluster; posts highlight an autonomous Kung Fu performance and an H2 “Monkey King” segment with robot dogs @XRoboHub.
• Qwen3.5 in evaluation venues: LM Arena added Qwen3.5-397B-A17B to Text/Vision/Code arenas and asked users to test and vote for leaderboards @arena.
• Anthropic research on skill retention: a post summarized an RCT where AI coding assistance decreased skill mastery by 17% among 52 software engineers, with debugging most affected despite minimal productivity gains @dl_weekly.
• Meta patent: a post claims Meta patented an AI that takes over a deceased person’s account to keep posting and chatting @kimmonismus.

Blackwell Ultra pushes agent economics: 50× throughput-per-megawatt, 35× lower token costs

NVIDIA: GB300 NVL72 + software stack targets low-latency, long-context agents

New SemiAnalysis InferenceX data highlighted by NVIDIA says GB300 NVL72 systems (Blackwell Ultra) can deliver up to 50× higher throughput per megawatt and 35× lower cost per token vs. Hopper, attributed to hardware–software codesign (including NVIDIA Dynamo and TensorRT-LLM) nvidia. NVIDIA also points to TensorRT-LLM improvements delivering up to 5× better performance on GB200 for low-latency workloads compared with four months ago nvidia.

Why it matters: Agentic coding and interactive assistants compound latency across multistep workflows, so the focus on low-latency + long-context economics is aimed at making “always-on” agents viable at scale nvidia.

Deployment signals (and what’s next)

NVIDIA says Microsoft Azure, CoreWeave, and Oracle Cloud Infrastructure are deploying GB300 NVL72 for low-latency and long-context use cases like agentic coding and coding assistants nvidia. Looking ahead, NVIDIA says the Rubin platform (described as combining six new chips “to create one AI supercomputer”) is set to deliver up to 10× higher MoE inference throughput per megawatt than Blackwell nvidia.

Why it matters: If these deployment claims hold, the “agent layer” push is increasingly tied to infrastructure rollouts, not just model releases nvidia.

Agents: capability jump meets practical security and memory design constraints

Security alert: 1-in-286 public agent “skills” found malicious

A large-scale security audit of public AI agent skill repositories reported 1-in-286 skills were malicious, with scripts designed to exfiltrate .env files and local API keysr/LocalLLM. Reported common vectors included unauthorized os.environ reads during routine tasks and authority hijacking via fake [SYSTEM] headersr/LocalLLM. A free scanner was released at https://agentshield.liver/LocalLLM.

Why it matters: As agents become more tool-enabled, “skills” and tool descriptions become part of the supply chain—and a direct target for credential theft r/LocalLLM.

A concrete memory/traceability proposal for long-running agents (from the OpenClaw ecosystem)

A design note shared in the OpenClaw orbit argues that between “the filesystem and the token window,” agents need an operational layer that selects/compresses context per turn and emits a manifest recording what was included/excluded and why @koylanai. The same thread proposes treating “everything as a file,” with a structured namespace for logs, episodic memory, durable facts, scratchpads, tools, and sessions—so context assembly becomes debuggable and memory changes become auditable@koylanai.

Why it matters: As token windows reset and sessions extend, systems that can answer “what did the agent load and what did it skip?” become operationally important—not just nice-to-have debugging tools @koylanai.

Anthropic expands in India (office + partnerships) amid highly technical usage

Bengaluru becomes a regional base; India is Claude’s #2 market

Anthropic announced it’s opening a Bengaluru office as its home base in India and its second office in Asia-Pacific@AnthropicAI. The company said India is its second-largest market for Claude and it’s launching partnerships to deepen its commitment @AnthropicAI.

Why it matters: This is a clear “on-the-ground” expansion signal in a market Anthropic is explicitly prioritizing @AnthropicAI.

Keynote metrics: revenue acceleration, enterprise training, and Indic language support

In an Anthropic Builder Summit keynote, the event stated India is the second largest user base for Claude globally, with run-rate revenue almost doubled since an October operations announcement, and that 6% of overall conversations come from India moneycontrol. The keynote also claimed 10 Indic languages have been added to Claude in the last six months moneycontrol and highlighted partnerships/training—e.g., a public-private effort where about 500,000 employees are being trained on Claude, alongside partnerships including Accenture, IBM, Cognizant, and Deloitte moneycontrol.

Why it matters: Anthropic’s India push is framed less as consumer growth and more as developer + enterprise deployment intensitymoneycontrol.

Benchmarks and research: testing what “agentic” and “reasoning” really mean

AIRS-BENCH: 20 agent tasks sourced from 17 ML papers

Import AI summarized AIRS-BENCH, which evaluates agents across 20 distinct tasks drawn from 17 recent ML papers, spanning areas like molecules/proteins, QA, time series, text classification, code, and math Import AI. Examples include generating multiple Python solutions per problem and time series forecasting on rideshare data Import AI.

Why it matters: The benchmark is explicitly about agents doing multi-step work across task types, not just single-shot Q&A Import AI.

“First Proof”: unpublished frontier math questions with encrypted answers

Import AI also covered First Proof, a set of ten math questions (from fields including algebraic combinatorics, algebraic topology, stochastic analysis, and more) where answers are known to the authors but kept encrypted briefly Import AI. The authors claim current top public systems “struggle” to solve many questions one-shot; Import AI notes GPT 5.2 Pro and Gemini 3.0 DeepThink did not solve it in their tests Import AI.

Why it matters: Keeping solutions off the open internet is an attempt to reduce contamination and test “frontier” problem-solving under cleaner conditions Import AI.

A caution on “reasoning gains”: are they confounded by corpus expansion?

A newly discussed paper asks how much apparent reasoning improvement is confounded by expanding training corpora 10,000×, and how much performance reflects “local” generalization via pattern-matching to semantically equivalent training data @g_leech_. Nathan Lambert highlighted it as work enabled by open efforts like OLMo @natolambert.

Why it matters: If true, it raises the stakes for evaluations that can separate durable reasoning from training-data adjacency effects @g_leech_.

AI-assisted physics: “gluon amplitudes” preprint backstory

Greg Brockman said a recent preprint on gluon amplitudes sparked discussion and that AI helped crack a problem that had stumped the team for a year @ALupsasca.

Why it matters: This is another data point for AI being used as a contributor inside technical research workflows—alongside ongoing debate about how to measure that reliably @ALupsasca.

Software engineering: “agent-first” pressures on languages, dependencies, and verification

Expert theses: monoliths return, the Lindy effect weakens, and types may matter more

Thomas Wolf argued that if rewriting and understanding large codebases becomes cheap, incentives to rely on deep dependency trees collapse—bringing a “return of monoliths” and a smaller supply-chain attack surface @Thom_Wolf. He also suggested the Lindy effect weakens (legacy code is easier to explore and replace), while emphasizing that unknown unknowns remain—and formal verification becomes essential in an AI-dominated world @Thom_Wolf.

Why it matters: This frames a plausible shift from “library ecosystems” to rebuild/verify pipelines—with security and correctness as first-class constraints @Thom_Wolf.

Karpathy + Carmack + Mojo: translation is the sweet spot, but speedup claims come with caveats

Andrej Karpathy argued LLMs are especially good at translation versus de-novo generation because the original codebase acts as a detailed prompt and a reference for tests; he also questioned whether Rust is optimal as a target language for LLMs @karpathy. In a related exchange, Chris Lattner said Mojo is being built as such a target language and claimed people are “one-shotting” large Python-to-Mojo conversions and seeing 1000× speedups@clattner_llvm. John Carmack responded that 1000× claims are “normally” unreliable, but starting from Python can make them possible @ID_AA_Carmack.

Why it matters: “Rewrite everything” becomes more credible if translation is robust—but the bar for trustworthy speed/correctness claims will rise alongside it @karpathy +1.

Autonomy in the real world: Waymo scales rides; Tesla tees up a steering-wheel-free vehicle

Waymo: sixth-gen platform enters “full autonomous operations”

A post said Waymo is starting “full autonomous operations” with its 6th gen platform@reed. Another post attributed to François Chollet reported Waymo does 500,000+ driverless rides per week and is “growing at 3× per year,” with the 6th gen platform reportedly costing ~$70k per vehicle (with potential to fall by 50% over two years) @fchollet.

Why it matters: The combination of ride volume and hardware cost trajectories points to autonomy progressing as an operational scale story, not only a model capability story @fchollet.

Tesla: Cybercab (no pedals, no steering wheel) “starts production in April”

Elon Musk wrote that Cybercab, described as having no pedals or steering wheel, starts production in April@elonmusk. A separate post noted Musk predicted “10 years ago” that cars would later have no steering wheels, and the audience laughed at the time @JonErlichman.

Why it matters: If production begins as stated, it’s a notable milestone for consumer-facing autonomy-focused vehicle design (at least in form factor) @elonmusk.

Societal and policy pressure: labor/capital splits, content backlash, and “timing AGI” arguments

Bengio: longer-horizon planning is growing fast; economic transition risk centers on capital capture

Yoshua Bengio said the “duration of the tasks” AIs can do is “doubling every seven months,” and suggested that if the curve continues, systems could reach “human level” in about five yearsSilicon Valley Girl. He also warned that as more jobs become doable by machines, gains may flow to “capital” (owners of machines), putting “the vast majority of workers” in trouble, and said governments haven’t thought carefully enough about the transition Silicon Valley Girl.

Why it matters: This pairs a concrete capability-growth claim with a distributional warning—an increasingly common framing as agentic automation moves from prototypes to workflows Silicon Valley Girl.

Khosla: tax policy tweaks as labor share declines

Vinod Khosla argued AI will transform economies and that labor’s portion of the economy (vs. capital) will “decline sharply,” suggesting equalizing capital gains tax with ordinary income and noting that 40% of capital gains taxes are paid by those with income >$10M/year@vkhosla. In a follow-up, he suggested equalizing capital gains tax and removing breaks could “eliminate bottom 125 million taxpayers from the tax rolls” while staying revenue neutral, and asked economists to check the math @vkhosla.

Why it matters: This is an example of AI-driven automation debates translating into specific—and contested—tax policy proposals @vkhosla +1.

ChinAI: public backlash to AI-generated culture content in China (#反ai)

ChinAI reported rising public resistance to AI-generated content in China, citing the #反ai hashtag on Xiaohongshu reaching 5.1M views and 40,000 discussion threads by January 2026 ChinAI Newsletter. It also described platform-level pressure: Tomato Novel saw 5,600+ new books through March 1, 2024 (vs. 400 in the same period in 2023), and Ximalaya’s AI-generated content rate reportedly hit 30% by April 2025 ChinAI Newsletter.

Why it matters: The “who wants this?” question is becoming visible in consumer platforms—not just in labor markets—through measurable backlash and moderation strain ChinAI Newsletter.

Bostrom: “swift to harbor, slow to berth” on AGI timing

Import AI highlighted Nick Bostrom’s view that the choice isn’t between a risk-free baseline and a risky AI venture, but between “different risky trajectories,” and that delays can increase suffering Import AI. He summarized his position as “swift to harbor, slow to berth”: move quickly toward capability, then potentially slow down during critical stages of scaleup and deployment Import AI.

Why it matters: The argument captures a live tension in the field: how to weigh near-term benefits against tail risks as capability and deployment speed increase Import AI.

Big Ideas

1) AI is shifting the PM job market from “nice-to-have” to table stakes

AI is now explicitly part of a large (and growing) share of PM hiring: 27% of open PM roles on LinkedIn mention AI (11,832 of 44,453), up from 20% last year, 10% the year before, and 2% earlier Aakash Gupta. Aakash Gupta frames this as a widening gap between PMs who have been building agents/running evals for years and late adopters Aakash Gupta.

Why it matters: “AI PM” is increasingly becoming PM, and companies are asking candidates and teams, “How well are you using and building AI?” Aakash Gupta.

How to apply: treat your AI skill-building like core craft development—ship something, learn the vocabulary, and build repeatable practices (e.g., evals, orchestration) rather than one-off prompt work Aakash Gupta.

2) A practical strategy model: three “juggling acts” (and how teams drift into crisis)

The Beautiful Mess offers a simple diagnostic: teams often operate in strategic juggling, lazy juggling, or survival jugglingThe Beautiful Mess.

• Strategic juggling preserves optionality intentionally, with explicit tradeoffs and periodic pruning/rebalancing The Beautiful Mess.
• Lazy juggling is novelty/anxiety-driven work without prioritization discipline or measurement The Beautiful Mess.
• Survival juggling is overload imposed by reality—tradeoffs are forced, and dropping anything has immediate consequences The Beautiful Mess.

Why it matters: teams can slip from strategic → lazy when pruning and learning loops break down The Beautiful Mess, and from lazy → survival when drift and deadlines converge The Beautiful Mess. Escaping survival mode requires painful tradeoffs and stabilization first The Beautiful Mess.

How to apply: use the assessment questions (e.g., “Which balls are true optionality vs fear-driven?”) to force an explicit reset conversation before reality forces one The Beautiful Mess.

3) If AI commoditizes product building, distribution + durable moats matter more

Lenny Rachitsky argues that as AI absorbs more of product building (from code completion through deciding what to build), the biggest human challenge/opportunity becomes distribution—getting attention in an ever-louder market@lennysan. He adds this dynamic tends to benefit incumbents/platforms, making it harder for startups to break through @lennysan.

Ryan Hoover similarly pushes back on “speed is the moat,” saying it’s “comical” when feature replication takes hours @rrhoover. He points to “real moats” like network effects, proprietary data, and regulatory licenses becoming more important @rrhoover.

Why it matters: when building gets cheaper/faster, differentiation shifts toward who can reach users (or embed into workflows) and who has structural defensibility.

How to apply: when evaluating bets, explicitly separate (1) build advantage vs (2) distribution advantage vs (3) defensibility—and don’t let “we’ll move fast” stand in for moat thinking @rrhoover.

4) “Scaling yourself” as a product leader is about support systems + ruthless delegation

A Teresa Torres episode observes product leaders often struggle to scale their impact by building a support network—e.g., it’s “super rare” for CPOs/Heads of Product to have executive assistants compared with CEO/CFO peers All Things Product with Teresa …. The core framing: focus on what you uniquely do, and delegate the rest to avoid calendar overload All Things Product with Teresa ….

Why it matters: senior scope expands faster than individual capacity; without support, leaders drift into survival juggling.

How to apply: consider lightweight help (even 3–6 hours/week) for calendar/email triage and boundary enforcement All Things Product with Teresa …, plus data/research support for strategy discovery work All Things Product with Teresa ….

5) Context reconstruction is becoming a first-class tax on PM execution

A solo PM describes reopening a long list of tools daily (Notion/Jira/Slack/GitHub/Linear/docs/customer notes) and spending ~30 minutes reconstructing context before doing useful work r/prodmgmt. The thread highlights that the energy drain is often rebuilding the mental model, not the work itself r/prodmgmt.

Why it matters: scattered context makes execution slower and increases decision churn (re-litigating “why did we do this?”).

How to apply: treat “fast access to decision context” as an operational requirement, not a personal productivity hack (see playbook below) r/prodmgmt.

Tactical Playbook

1) Dialectical Bootstrapping for better estimates (and deeper AI-assisted reasoning)

Dialectical Bootstrapping is described as a systematic “prompt” to think through plausible alternatives and then provide a second guess, improving estimates from stakeholders/devs r/ProductManagement.

Steps

1. Ask for a first estimate/plan.
2. Force at least 2–3 plausible alternatives (different approaches, constraints, or hidden dependencies) r/ProductManagement.
3. Re-estimate with those alternatives in mind (the “second guess”) r/ProductManagement.
4. Capture the assumptions behind the estimate so the team can revisit them when reality changes.

Why it matters: it makes “what could be true instead?” a default step—useful with humans and when prompting agents.

2) A roadmap-safe way to handle “urgent” stakeholder change requests

A product-specialist interview scenario: a stakeholder change request would reduce manual processes and is perceived as urgent for service standards, but it conflicts with a 2-month prioritized roadmap with aligned backlogs r/prodmgmt.

Steps

1. Validate the request (what problem, for whom, and what makes it urgent?) r/prodmgmt.
2. Align on requirements with the team (what exactly must change; what’s in/out?) r/prodmgmt.
3. Prioritize against existing commitments (explicit tradeoff vs the current roadmap) r/prodmgmt.
4. Make assumptions explicit (e.g., operational impact, service-standard risk, adoption) r/prodmgmt.
5. List other considerations that could change sequencing (dependencies, risk, capacity, governance) r/prodmgmt.

Why it matters: it converts “urgent” into a structured decision instead of a bypass around prioritization.

3) Don’t let churn become a Rorschach test—unpack what it represents

A Reddit post argues that not all early churn signals product failure; it can reflect poor segment fit, exploratory users, or acquisition misalignment r/prodmgmt. The same churn number can trigger very different interpretations across a team r/prodmgmt.

Steps

1. Before labeling churn as failure, test which bucket it’s in: segment fit vs exploratory behavior vs acquisition mismatch r/prodmgmt.
2. Surface competing interpretations explicitly (what story is each function telling with the same number?) r/prodmgmt.
3. Decide what additional evidence would disconfirm each story (so you’re not overinterpreting) r/prodmgmt.

Why it matters: churn is an outcome metric; without context, teams can “fight the narrative” instead of fixing the underlying problem.

4) Capture spontaneous feedback without attracting discount spam

A PM asked for tools that help gather spontaneous feedback after discount-based incentives produced spam/fake answers r/ProductManagement. Suggestions included:

Option A: On-page micro-surveys (Hotjar)

1. Add an on-page survey widget.
2. Ask users to rate the experience, then optionally provide details (a slide-up window in the bottom-right was cited) r/ProductManagement.

Option B: Lightweight feedback ops (modu.io)

• Use modu.io for feedback boards + roadmaps, surveys, changelogs, and ratings r/ProductManagement.

Why it matters: “in-the-moment” feedback can reduce incentive-driven gaming.

5) Delegation that actually sticks: the “80% rule” + mixed AI/human support

The Teresa Torres conversation offers a delegation mindset: “80% done by someone else is 100% awesome”—a way to overcome perfectionism and offload the first 80% of work All Things Product with Teresa …. It also notes leaders may think “AI can do it,” but a human assistant can still help by taking work off your plate (including delegating some AI workflow building) All Things Product with Teresa ….

Steps

1. Identify what value you uniquely bring, and protect time for it All Things Product with Teresa ….
2. Delegate work that others can do—even if it comes back at ~80% quality All Things Product with Teresa ….
3. Use support to enforce boundaries (e.g., an EA saying “no” on your behalf) All Things Product with Teresa ….
4. Learn delegation patterns from peers/mentors (not just trial-and-error) All Things Product with Teresa ….

Case Studies & Lessons

1) Shopify operationalizes “AI-first” in performance reviews and staffing

Shopify reportedly includes AI usage in performance reviews, requires teams to prove AI can’t do the job before requesting headcount, and has cut its workforce by about a third since 2022 while selectively investing in AI engineers at higher compensation Aakash Gupta.

PM takeaway: incentives and operating mechanisms (reviews, headcount gates) can rapidly change day-to-day behavior—faster than “AI strategy” decks.

2) When “disciplined optionality” becomes exhaustion: a multi-bet strategy failure mode

A case describes leadership launching several major bets to hedge uncertainty; in practice, teams felt stretched, the core product slipped, and none of the initiatives achieved decisive traction The Beautiful Mess. The diagnosis hinged on an unproven assumption that one bet would eventually break out—yet the organization was too tired from juggling to run disciplined learning loops The Beautiful Mess.

PM takeaway: optionality only stays strategic if you maintain pruning and learning mechanisms; otherwise it drifts toward “wait and see” and burnout The Beautiful Mess.

3) Buildathon evidence: PMs can ship real agents quickly—with real product thinking

In The Product Compass’s AI Agents Buildathon for PMs, 36 teams shipped working AI agents (not mockups), including value propositions, differentiation, defensible moats, and designed guardrails/autonomy boundaries The Product Compass. The post encourages studying how teams set autonomy boundaries and handled tradeoffs and real-world challenges The Product Compass.

PM takeaway: the bar is moving from “AI ideas” to “AI systems with constraints, evals, and clear value.”

4) From information to execution: OpenClaw as an agent-driven operations layer

A session on OpenClaw describes using an AI assistant to scan meeting transcripts/documents and Asana tasks to identify what was missed and auto-add items to the Asana board, framing the shift as “information to execution” Product Management Exercises. It also calls out practical constraints: no monthly SaaS fees, but LLM usage can get expensive (e.g., $200 in two days), prompting a cost strategy using different models for different tasks Product Management Exercises.

PM takeaway: “agentic” workflows get real when they write back to systems (tasks, calendars) and when you design for cost/control upfront.

5) Solo PM reality: PRDs are easy; rebuilding context is the work

The solo-PM thread summarizes the pain succinctly: “Writing the actual PRD is easy. Rebuilding the context behind it is the real work.” r/prodmgmt

PM takeaway: invest in whatever reduces “context fetch time,” or you’ll spend your best hours re-deriving decisions.

Career Corner

1) The AI PM skill stack is becoming a distinct vocabulary (and hiring filter)

Aakash Gupta highlights that AI-related PM roles increasingly require vocabulary such as context engineering, orchestration, observability, evals, and that many PMs still struggle to explain the difference between RAG vs. fine-tuningAakash Gupta. He also notes traditional companies cutting PMs (e.g., FedEx, UPS, Target) while AI leaders (OpenAI, Google) and AI arms hire them Aakash Gupta.

How to act this quarter:

• Prioritize one “proof of work” project that forces you to use evals/agent constraints in practice.
• Benchmark your skills against job market reality (27% already AI-tagged) Aakash Gupta.

2) “Ship” beats “study” for confidence with technical counterparts

A testimonial from a Director of Automation describes discomfort engaging engineers/architects on GenAI due to lack of understanding, and says confidence improved dramatically after a cohort that introduced concepts and required applying them by the end Product Management Exercises.

How to apply: choose learning formats that end with building something (even a small prototype) so you can ask better questions and evaluate where an ML solution fits Product Management Exercises.

3) Advancing as a leader: build a support system (and use AI + humans together)

The Teresa Torres episode recommends considering:

• An executive assistant (even part-time) to manage calendar/email and enforce boundaries All Things Product with Teresa …
• A researcher/data person for strategy discovery and KPI analysis All Things Product with Teresa …
• Coaches for clarity, public speaking, leadership, and accountability All Things Product with Teresa …
• A community of practice for onboarding, candidate pre-qualification, and role descriptions All Things Product with Teresa …

Tools & Resources

• AI PM market + skills roundup (Aakash Gupta)Aakash Gupta

  • Context engineering: https://www.news.aakashg.com/p/rag-vs-fine-tuning-vs-prompt-engineeringAakash Gupta
  • AI evals: https://www.news.aakashg.com/p/ai-evalsAakash Gupta
  • AI agents: https://www.news.aakashg.com/p/ai-agents-pmsAakash Gupta
  • How to become an AI PM: https://www.news.aakashg.com/p/how-to-become-and-succeed-as-an-aiAakash Gupta

• AI Agents Buildathon solution gallery (The Product Compass): https://www.productcompass.pm/p/ai-solutions-galleryThe Product Compass

  • Intent engineering framework: https://www.productcompass.pm/p/intent-engineering-framework-for-ai-agentsThe Product Compass
  • Context engineering guide: https://www.productcompass.pm/p/context-engineeringThe Product Compass
  • AI product strategy (moats/costs/distribution): https://www.productcompass.pm/p/openai-how-to-build-ai-product-strategyThe Product Compass
  • Evaluating AI agents (evals): https://www.productcompass.pm/p/how-to-evaluate-ai-agents-n8nThe Product Compass

• Support systems for product leaders (YouTube): https://www.youtube.com/watch?v=ayKoE1MvxpMAll Things Product with Teresa …

• OpenClaw for product managers (YouTube): https://www.youtube.com/watch?v=gINPJWwFVLsProduct Management Exercises

• Spontaneous feedback collection (thread + tools)

  • Hotjar on-page surveys (slide-up rating + details) r/ProductManagement
  • modu.io: http://modu.ior/ProductManagement

• Threads worth scanning

  • Dialectical Bootstrapping (estimation + deeper thinking): https://www.reddit.com/r/ProductManagement/comments/1r6plne/r/ProductManagement
  • Prioritizing change requests (interview scenario): https://www.reddit.com/r/prodmgmt/comments/1r6rahx/r/prodmgmt
  • Early churn nuance: https://www.reddit.com/r/prodmgmt/comments/1r6fj1j/r/prodmgmt
  • Solo PM context rebuilding: https://www.reddit.com/r/prodmgmt/comments/1r6nen9/r/prodmgmt

Most compelling recommendation (strongest endorsement)

“Software moats in the AI era” (X post/article)

• Title: Software moats in the AI era (as described by the recommender) @patrick_oshag
• Content type: X post / X article @patrick_oshag +1
• Author/creator: @nicbstme @patrick_oshag
• Link/URL:
  • Post: https://x.com/nicbstme/status/2023501562480644501@patrick_oshag
  • Article: http://x.com/i/article/2023495526877155328@nicbstme
• Who recommended it: Patrick O’Shaughnessy (@patrick_oshag) @patrick_oshag
• Key takeaway (as shared): O’Shaughnessy calls it “The best post I’ve read on software moats in the AI era”@patrick_oshag.
• Why it matters: It’s a high-conviction pointer to a single piece on how defensibility/moats may be changing under AI, from an investor explicitly ranking it above other reads in the same category @patrick_oshag.

Two more high-signal reads (strategy + valuation)

CAP (PDF): durability and “terminal value” in valuations

• Title: CAP (PDF) @bgurley
• Content type: PDF @bgurley
• Author/creator: Not specified in the source excerpt @bgurley
• Link/URL: https://pages.stern.nyu.edu/~adamodar/pdfiles/eqnotes/cap.pdf@bgurley
• Who recommended it: Bill Gurley (@bgurley) @bgurley
• Key takeaway (as shared): Gurley frames it as a lens for valuation in a world where “so much value is in terminal value,” raising the question: “will this company be around in 30 years?”—and notes this is an “easy question for Walmart,” in the context of comparing Walmart/Costco multiples vs. software companies @bgurley. He also adds: “AI causes CAP anxiety.”@bgurley
• Why it matters: It’s a direct pointer to thinking about business durability when long-duration assumptions dominate valuation—and how AI may be pressuring those assumptions @bgurley +1.

The Cathedral and the Bazaar (essay/paper): open source as a competitive weapon

• Title: The Cathedral and the Bazaar @bgurley
• Content type: Essay/paper (PDF) @bgurley
• Author/creator: Not specified in the source excerpt @bgurley
• Link/URL: https://users.ece.utexas.edu/~perry/education/382v-s08/papers/raymond.pdf@bgurley
• Who recommended it: Bill Gurley (@bgurley) @bgurley
• Key takeaway (as shared): Gurley argues people “don’t understand what a competitive strategic weapon open source has become and how it works,” and says it’s “always good to reread the cathedral and bazaar” @bgurley.
• Why it matters: A reminder that open source can be a strategic instrument, not just a development model—worth revisiting if you’re thinking about competition in software markets @bgurley.

Market Movers

Soybeans & grains (U.S. / global)

• Soybean positioning and demand signals: With U.S. grain and soybean trading closed for Presidents Day, one market summary noted that investors raised bullish bets on soybeans@SuccessfulFarm. Separately, a commodity outlook tied recent soybean price strength to U.S.–China shipments gaining pace, alongside attention to potential La Niña-induced drought risks for U.S. farmers going into springThe Commodity Report.
• Trade headline risk: In a policy interview, the host referenced President Trump saying China committed to buying an additional 8 million metric tons of soybeans this marketing year; the interviewee framed it as trust but verify and emphasized the need for enforceable trade mechanisms Brownfield Ag News.
• Storage and supply backdrop: A separate D.C. policy discussion noted higher-than-normal grain balances after large U.S. wheat/soy/corn harvests, though much of the grain on the ground has been moving through the channel Brownfield Ag News.

Longer-cycle pricing

• A Successful Farming post linked to an expert view that corn and soybean prices are in the trough of the current cycle, with the possibility they begin to rise in 2027@SuccessfulFarm.

Livestock

• A cattle industry outlook cited low cow inventory and resilient beef demand as supporting near- to mid-term strength Farm4Profit Podcast. A separate commodity note added that the fundamental case for higher cattle prices remains intact and that herd rebuilding can take a decadeThe Commodity Report.

Agricultural inflation (Turkey)

• Turkey’s Agricultural Producer Price Index (Tarım-ÜFE) rose 8.46% month-over-month and 43.58% year-over-year in January; the sharpest monthly increases cited were 30.43% (vegetables/melons/roots & tubers) and 30.11% (citrus) @irfandonat.

Innovation Spotlight

Precision application + automation on a North Dakota operation (U.S.)

A producer in west central North Dakota described measurable outcomes from John Deere automation and targeted-application tools:

• See & Spray: Reported a 96% chemical savings in a low-weed field, plus improved visibility into where weeds persist through weed-density mapping RDO Equipment Co.. The same operator highlighted the learning curve around selecting spray tips and trusting the system RDO Equipment Co..
• ExactShot fertilizer placement: Reported using 0.8 gallons/acre of 10-34-0 on corn (equated to five gallons/acre), with no yield drop versus prior practice while retaining fertilizer savings RDO Equipment Co..
• Harvest automation: Automation revealed the cost of pushing late—estimated 0.5 to 1 bushel/acre thrown over in lower-light / changing-wind conditions—leading to earlier shutdown decisions RDO Equipment Co..

Organo-mineral and “special fertilizers” (Brazil)

• Brazil’s “special fertilizers” segment (including organo-minerals) was described as having grown ~19% in 2024, with continued expansion expected into 2026Canal Rural.
• In Santa Catarina, Fecoagro (11 cooperatives) was highlighted using organo-minerals, including inputs derived from leather waste processed via hydrolysis to produce amino acids and organic nitrogen incorporated into fertilizer formulations Canal Rural.
• The approach was framed as integration with conventional fertilizers (not replacement) to improve nutrient-use efficiency and reduce losses, including via slower nutrient release Canal Rural.

Coffee climate-risk and compliance tooling (Brazil / EU-linked)

• Minas Gerais coffee stakeholders were offered a platform from the University of Prague integrating satellite data and geospatial information to help assess water deficit, insolation, and vulnerable areas for climate-impact mitigation Canal Rural.
• The Comunidade platform (already applied in Colombia and Chile) was presented as supporting field decisions such as water management and climate-risk mitigationCanal Rural. Minas Gerais also cited its Selo Verde MG platform to expand traceability and support EU export complianceCanal Rural.

Livestock analytics for real-time breakevens (U.S. / livestock)

• Performance Livestock Analytics described its Performance Beef platform starting with iPad-based feed tracking to capture every pound loaded and delivered to bunks Farm4Profit Podcast.
• A chute-side module using UHF tags was described as enabling hands-free capture of nutrition, health, and financial data and providing real-time breakevensFarm4Profit Podcast.
• A related “auction advisor” tool was described as calculating breakevens in real time to avoid overbidding at auction Farm4Profit Podcast.

Regional Developments

Brazil: Mato Grosso harvest delays and safrinha risk

• Excessive rains in Mato Grosso were reported to be delaying soybean harvest and pushing second-crop corn (safrinha) planting outside the safer window Canal Rural.
• In Paranatinga, early corn plantings were described as flooded (plants failing to emerge/develop after six days of rain), and planned safrinha area was expected to be reduced from 300–320 hectares, implying lower production Canal Rural. A nearby property cited 1,800 mm accumulated rainfall versus a 2,200–2,400 mm historical range Canal Rural.
• Weather coverage also described only a short window to accelerate soybean harvest and corn planting before heavier rains return (volumes cited >280 mm starting around Feb. 20–21) Canal Rural.

Brazil: pests, costs, and fitosanitary pressure in soy

• In Mato Grosso, soybean production costs were cited at R$6,000–7,000 per hectare, with ~30% concentrated in agricultural defensives Canal Rural.
• A highlighted emerging concern was mosca-da-larva-minadora, described as reducing leaf area, shortening the cycle, and compromising grain fill; the damage was also linked to opportunistic diseases such as mancha-alvo and cercosporoseCanal Rural.
• Soil nematodes were described as major yield threats; estimates cited R$36 billion in damages across crops per season and ~R$16 billion for soy alone, with Pratylenchus emphasized in Mato Grosso soils Canal Rural.

Brazil: export performance and market diversification

• Brazil’s agribusiness exports totaled US$10.8B in January 2026, down 2.2% YoY despite +7% volume, attributed to an 8.6% decline in average prices amid lower international commodity prices Canal Rural +1.
• China (20%), the EU (11%), and the U.S. (6.6%) were cited as top buyers; exports to ASEAN grew 5.7% in January 2026 vs. a year earlier Canal Rural. Beef exports were highlighted at US$1.3B and 231k tons shipped to 116 countries, with U.S. purchases up 93% in January Canal Rural.
• Coverage of Brazil’s upcoming mission to India and South Korea framed the objective as diversifying partners; India exports were cited at ~US$7B in 2025 (including sugar and soy oil) with a stated target of US$20B in coming years Canal Rural.

U.S.: processing capacity and trade-policy calendar

• Smithfield Foods was reported to be planning a new processing facility in Sioux Falls, South Dakotar/farming.
• A U.S. policy interview noted the USMCA is entering a formal review process, with a joint review expected to begin July 1; the interviewee argued the agreement has been “fantastic” for U.S. agriculture and emphasized extending it for the full 16 years, with dairy cited as an area to improve Brownfield Ag News.

Best Practices

When corn yields are strong but soybeans disappoint (field diagnostics)

A practical checklist for “great corn, poor soybeans” emphasized starting with:

• Iron deficiency chlorosis; insects (e.g., gall midge larvae); soybean cyst nematodes; diseases (white mold, pythium, sudden death syndrome); planting timing; and weed control @AgPhDMedia.
• The most common drivers cited were poor drainage and potassium. Soybeans were described as having one-fifth the root mass of corn, making them more sensitive to drainage issues, and as needing more than double the potassium per day (at peak) compared with corn @AgPhDMedia.
• A field-proven fix cited was installing tile and applying substantial potassium, which was said to have nearly doubled soybean yields a decade earlier @AgPhDMedia.

Broiler pre-slaughter handling (Brazil)

Operational steps highlighted to reduce condemnations and protect carcass quality:

• Fasting: Follow integrator timing with partial feed-cut so total fasting does not exceed 12 hours; water remains available Canal Rural.
• Movement + water access: Gentle movement plus adequate drinker flow to support excretion; mishandling was linked to stress, piling, scratches, dermatitis, and issues such as “full rectum” at the plant Canal Rural.
• Loading: Box birds by weight (example given: 6–9 birds per box) and prioritize cooler hours; post-loading truck wetting (“molha-frango”) was described as helping cooling and welfare in transit Canal Rural.

Pasture brush control (U.S.)

• Options cited for controlling woody brush included burning, herbicides, and using goats@SuccessfulFarm.

Swine carcass composting (Brazil; Embrapa method)

• A method described layering 60 cm substrate, placing a carcass (with perforation in the abdomen to prevent bloating), covering with another 60 cm substrate, and humidifying only in very dry regions; it was described as avoiding odor/flies/contamination via substrate absorption Canal Rural.

Input Markets

Fertilizer and crop protection

• Brazil (soy): Field cost estimates of R$6,000–7,000/ha, with ~30% in defensives, underscore the economic weight of pest/disease management Canal Rural.
• Brazil (fertilizers): Special fertilizers were cited as growing ~19% in 2024 with momentum expected through 2026Canal Rural.
• U.S. (inputs): A D.C. policy interview cited rising costs across fertilizer, seed/chemistry, land, interest rates, and agricultural labor, and noted the administration removed tariffs on fertilizer imports as relief Brownfield Ag News.

Labor

• A California specialty-crop operator cited minimum wage rising to $17, with overtime starting at 40 hours, describing labor costs as “astronomical” for labor-intensive crops Farm Journal.

Product/application selection reminders

• An Ag PhD post highlighted differences between zinc sulfate and zinc EDTA (zinc sources) @AgPhDMedia.
• Another Ag PhD clip noted “many variations” in wheat herbicides@AgPhDMedia.

Forward Outlook

• Brazil (Mato Grosso): Watch for continued harvest disruption as rain is expected to intensify again (including a short harvest/planting window before heavier totals cited around Feb. 20–21) Canal Rural. More broadly, Canal Rural weather coverage flagged that rains could tighten again Feb. 22–26, with soybean harvest areas already experiencing saturated soils Canal Rural.
• Row-crop planning (U.S.): Spring crop-mix decisions were described as often coming late; one policy interview suggested farmers are watching South American supply and markets to choose the right corn/soy mix Brownfield Ag News.
• Price-cycle context: A cited view places corn/soy prices in a cyclical trough, with a possible upswing not expected until 2027@SuccessfulFarm.
• Trade calendar (North America): The USMCA joint review process is expected to start July 1, with calls to extend the agreement for the full 16 yearsBrownfield Ag News.
• Commodities allocation theme (2026): One commodity note expects 2026 returns to be driven largely by agri-commodities rather than metals The Commodity Report.