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Gemini 3.1 Pro ships: benchmark jump + broad rollout

Gemini 3.1 Pro hits 77.1% on ARC-AGI-2

Google leaders say Gemini 3.1 Pro reaches 77.1% on ARC-AGI-2, described as more than 2× Gemini 3 Pro’s performance and a step forward in core reasoning @sundarpichai +2. DeepMind adds that ARC-AGI-2 tests novel logic patterns and that the model is aimed at workflows “where a simple answer isn’t enough” @GoogleDeepMind.

Why it matters: This is one of the clearest “headline” reasoning deltas in a mainstream model launch, and it immediately feeds into ongoing questions about what different evals actually capture (see “Evals” below). r/LocalLLM

Availability: Gemini App, NotebookLM, API preview, and enterprise

Google says Gemini 3.1 Pro is rolling out across multiple surfaces:

• Gemini App@GoogleDeepMind +1
• NotebookLM (exclusive to Google AI Pro/Ultra users) @GoogleDeepMind
• Developers via Gemini API preview in Google AI Studio@GoogleDeepMind +1
• Enterprises via Vertex AI and Gemini Enterprise@sundarpichai

Perplexity also upgraded Gemini 3 Pro → Gemini 3.1 Pro for all Pro/Max users (consumer and enterprise), and says it’s the second most picked model by its enterprise customers after the Claude 4.5 Sonnet/Opus family @AravSrinivas.

Why it matters: Distribution is not confined to one product—Google is pushing the same model into consumer, developer, and enterprise channels in parallel, with immediate third-party adoption. @sundarpichai +1

Demos: city planning, CAD → analysis, and SVG generation

Google and DeepMind showcased several “complex workflow” examples:

• A city planner app where the model handles complex terrain, maps infrastructure, simulates traffic, and produces visualizations @GoogleDeepMind. Jeff Dean also shared an urban planning simulation example for designing new cities @JeffDean.
• A “Deep Think” workflow (described as no tools, using Deep Think + image generation) that: generates a CAD file from a technical drawing, runs heat transfer analysis, and turns results into time-step visualizations@JeffDean +2.
• Improved SVG generation, including examples of prompt-to-SVG and follow-up edits @OriolVinyalsML +1. Another demo claims Gemini 3.1 Pro can generate web-ready animated SVGs from text prompts @addyosmani.

Why it matters: The messaging is less “chat answerer” and more “workflow engine”—including structured artifacts (CAD/SVG) and multi-step analysis/visualization. @JeffDean +1

Agentic engineering: post-IDE tools, compute constraints, and new security pitfalls

“Post-IDE” agent development environments (ADEs) keep solidifying

@swyx argues the shift to post-IDE agentic development environments is now “here,” pointing to Augment’s Intent as a consolidation of multiple code-agent management ideas (while not locking users into a single in-house agent) @swyx.

Why it matters: The competitive surface is moving from model quality alone to how agents are orchestrated and managed in day-to-day engineering workflows. @swyx

Agentic coding as “machine learning,” with ML-style failure modes

François Chollet frames sufficiently advanced agentic coding as essentially machine learning: engineers define an optimization goal + constraints (spec/tests), agents iterate, and the result is a black-box codebase often deployed without inspecting internal logic @fchollet. He warns classic ML issues will show up: overfitting to specs, “Clever Hans” shortcuts, data leakage, and concept drift @fchollet.

Why it matters: If codebases start to resemble trained artifacts, teams may need higher-level abstractions to steer “codebase training” and to manage reliability beyond conventional code review. @fchollet

Inference compute is becoming an explicit productivity bottleneck

Greg Brockman says the inference compute available to you will increasingly drive software productivity @gdb. He highlights an interview trend: candidates are being asked how much dedicated inference compute they will have for building with Codex, as usage per user grows faster than the user count—suggesting compute scarcity @gdb +1.

Why it matters: If teams treat inference capacity as a primary constraint, “agent throughput” could become a core planning variable alongside headcount and budgets. @thsottiaux

Tool-calling vulnerability: models may invoke tools you didn’t provide

Jeremy Howard points to a tool-calling issue where an LLM given a list of tools it’s allowed to call might decide to call a tool you didn’t provide@jeremyphoward. He says this impacts major labs (Anthropic, xAI, Gemini) and “all major US providers except OpenAI,” advising developers to check tool call requests@jeremyphoward.

Why it matters: As agents get more permissions, tool invocation becomes an access-control boundary—and failures here can turn “helpful automation” into unauthorized actions. @PiotrCzapla +1

Evals, governance, and “what’s actually happening in the world”

The eval mismatch shows up immediately: ARC-AGI-2 vs Arena (and “saturated” tests)

A LocalLLM post notes Gemini 3.1 Pro “just doubled its ARC-AGI-2 score,” while Arena still ranks Claude higher, calling it “exactly the AI eval problem” r/LocalLLM. Separately, a thread comments that a named eval was “saturated,” with criticism that lab leaders publicly tweeting about an eval implies it was (at minimum informally) targeted @HamelHusain +1.

Why it matters: Model comparisons are increasingly gated by which benchmark you trust—and by whether the evaluation itself stays robust under optimization pressure. r/LocalLLM +1

Government/standards groups push toward private testing + decision-linked benchmarks

From a panel on international evaluation practices, Sarah Hooker argues benchmarks are in a “muddy middle”: static, quickly overfit, and often gamified—supporting a return to private test sets and no-notice testing Diya TV. She also argues benchmarks should guide decisions—otherwise you’re “just collecting data” Diya TV.

Why it matters: As governments embed AI deeper into critical systems, evaluation regimes may shift toward private, operationally-relevant testing rather than public leaderboards. Diya TV +1

Anthropic scales its “Societal Impacts” team

Anthropic says it’s “aggressively scaling up” its Societal Impacts team as models begin having “non-trivial impacts on the world” @jackclarkSF. The team focuses on testing properties, building observation tools, and generalizing them across the org, including work supporting the Anthropic Economic Index and studying agents “in the wild” @jackclarkSF +1.

Why it matters: This is a sign that post-deployment measurement and feedback loops are becoming a first-class capability alongside model development. @jackclarkSF

India summit signals: competing timelines, diffusion focus, and coordination proposals

Altman: democratization, disruption, and an IAEA-like coordination concept

Sam Altman says OpenAI believes it may be “only a couple of years away” from early versions of true superintelligence, with the caveat they could be wrong; he adds that by end of 2028, more of the world’s intellectual capacity “could reside inside of data centers” than outside ANI News. He also calls for something “like the IAEA” for international coordination of AI with the ability to respond rapidly to changing circumstances CNBC-TV18.

Why it matters: This pairs aggressive capability timelines with explicit institutional proposals for cross-border coordination, reflecting how fast “governance architecture” is being pulled into mainstream leadership messaging. CNBC-TV18

Bengio: global, UN-rooted science-policy interface and “policy lag” risk

Yoshua Bengio argues AI capabilities are growing rapidly but unevenly, while scientific studies and policy processes create a lag that can become dangerous if things move too fast DRM News. He highlights the importance of a UN-rooted international panel and multidisciplinary work so “everyone is at the table and no one is on the menu” DRM News.

Why it matters: The emphasis is less on settling predictions and more on building mechanisms that can act under uncertainty—especially for high-severity risks. DRM News

Product + platform moves worth tracking

Microsoft adds xAI’s Grok 4.1 Fast to Copilot Studio

Microsoft says it’s adding xAI’s Grok 4.1 Fast to the multi-model lineup in Copilot Studio, positioning it as more choice/flexibility for building custom agents @satyanadella. Elon Musk also says Grok 4.20 is “coming soon” @elonmusk.

Why it matters: Multi-model “agent builders” are turning model choice into a platform feature—shifting competition toward orchestration, governance, and enterprise packaging. @satyanadella

Perplexity: Comet iOS pre-order + Finance auditability into SEC filings

Perplexity’s CEO says Comet (an iOS AI personal assistant/browser) is nearly ready and available for pre-order, aiming for a “Safari grade browser” with Perplexity powering each webpage and providing assistance @AravSrinivas. Separately, Perplexity Finance now includes tap-through auditability to SEC filings, pre-scrolled to the page where a cited line item appears @jeffgrimes9.

Why it matters: “Answer engines” are pushing deeper into verifiable workflows (finance audit trails) while also experimenting with new assistant-native browsing surfaces. @jeffgrimes9 +1

Research notes (fast scans)

Interpretability: “Cheap Anchor V2” predicts circuit edge importance from weights alone

A MachineLearning subreddit post reports “Cheap Anchor V2,” which predicts causal edge importance in GPT-2 small’s induction circuit using a composite of discrimination (spectral concentration) and cascade depth (downstream path weight) r/MachineLearning. It reports Spearman ρ=0.623 vs. path-patching ground truth with a 125× speedup (2s vs 250s), beating weight magnitude and gradient attribution baselines r/MachineLearning.

Why it matters: If reproducible, this suggests a cheaper pre-filter for mechanistic interpretability—scoring many candidate edges before spending expensive intervention compute. r/MachineLearning

Open neuromorphic processors: Catalyst N1/N2 claim Loihi parity + FPGA validation

A separate post introduces Catalyst N1 & N2, open neuromorphic processors aiming for feature parity with Intel Loihi generations, with N2 adding programmable neurons (five shipped models) and reporting FPGA integration tests with zero failures r/MachineLearning. It reports 85.9% SHD accuracy (float) and 85.4% (16-bit) r/MachineLearning.

Why it matters: This is a notable “open hardware + full stack” claim (papers, SDK, FPGA tests) in a space typically dominated by proprietary chips and platforms. r/MachineLearning

Small open-weight multilingual model: Tiny Aya (3.35B)

Sebastian Raschka highlights Tiny Aya (3.35B) from Cohere as a small open-weight model with strong multilingual support in its size class, suitable for on-device translation @rasbt. He calls out architectural choices like parallel transformer blocks, sliding window attention (4096 window; 3:1 local:global), and a modified LayerNorm without bias @rasbt.

Why it matters: Continued innovation in small-model architecture suggests the “open + on-device” track is still moving quickly alongside frontier scaling. @rasbt

🔥 TOP SIGNAL

Boris Cherny (Head of Claude Code) is blunt: for the kinds of programming he does, “coding is largely solved”, and the frontier is shifting to adjacent, end-to-end agentic work (project management, paying tickets, general ops) rather than better IDE autocomplete Lenny's Podcast. In that world, throughput isn’t hypothetical: he says Anthropic saw +200% productivity per engineer (PRs)Lenny's Podcast, and Claude now reviews 100% of pull requests (with human review still in the loop) Lenny's Podcast.

🛠️ TOOLS & MODELS

• Claude Code — stability + performance signals

  • v2.1.47: long-running sessions use less memory@jarredsumner.
  • Team guidance: keep reporting issues and they’ll fix them @jarredsumner.
  • Practitioner complaint: Theo reports Claude Code has “regressed an absurd amount” with UI/feedback issues (timestamps not updating, missing “thinking,” multi-minute hangs with 0 output) @theo and suggests it “needs to be rewritten from scratch” @theo.

• Cursor — agent sandboxing shipped across desktop OSes

  • Cursor says it rolled out agent sandboxing on macOS, Linux, and Windows over the last three months @cursor_ai.
  • Mechanism: agents run freely inside a sandbox, only requesting approval when they need to step outside it @cursor_ai.
  • Implementation write-up: http://cursor.com/blog/agent-sandboxing@cursor_ai.

• OpenAI Codex — pricing/availability + compute pressure

  • @thsottiaux: Codex is included with a ChatGPT subscription (even Plus has “very generous” usage) @thsottiaux; they attribute this to gpt-5.3-codex achieving “SoTA at lower cost” @thsottiaux.
  • Same source: candidates increasingly ask how much dedicated inference compute they’ll have, and usage/user is growing faster than user count → compute could be scarce@thsottiaux.

• Gemini 3.1 Pro — dev-workflow positioning (ramping up)

  • Google Antigravity: Gemini 3.1 Pro is ramping to Google AI Ultra/Pro users, pitched around “advanced reasoning” and “long horizon planning” for dev workflows @antigravity. Details: https://antigravity.google/blog/gemini-3-1-pro-in-google-antigravity@antigravity.

• GitHub Copilot → Zed editor (GA)

  • GitHub: Copilot subscription support in Zed is generally available @github. Changelog: https://github.blog/changelog/2026-02-19-github-copilot-support-in-zed-generally-available/@github.

• Model choice drift + self-hosting pressure (reported trend)

  • Salvatore Sanfilippo says he’s seeing excellent programmers move off US models (Codex, Claude Code) toward Chinese open-weight models like Kimi 2.5 and GLM5Salvatore Sanfilippo, often via providers or by building in-house Nvidia GPU inference to avoid outages and keep sensitive data internal Salvatore Sanfilippo.
  • He frames DeepSeek v4 as a potentially major moment if it lands as SOTA (as rumors suggest), putting pressure on OpenAI/Anthropic business sustainability Salvatore Sanfilippo.

💡 WORKFLOWS & TRICKS

• “Plan mode → execute” as a default loop (Claude Code / Boris Cherny)

  1. Start the task in plan mode (he says he does this for ~80% of tasks) Lenny's Podcast.
  2. Iterate on the plan (model goes back-and-forth) Lenny's Podcast.
  3. Once the plan is good, let it execute; he’ll auto-accept edits after that Lenny's Podcast.
  • Implementation detail: plan mode is literally a prompt injection: “please don’t write any code yet” Lenny's Podcast.

• Parallel agents, but treat “state” as a first-class problem

  • Cherny: he runs ~5 agents in parallel while working (terminal/desktop/iOS) Lenny's Podcast and highlights you can run many sessions in parallel Lenny's Podcast.
  • Kent C. Dodds: similar “utter chaos” workflow—multiple projects, “a couple cloud agents” each, plus a locally guided agent @kentcdodds.
  • Failure mode (real): Simon Willison describes “parallel agent psychosis”—losing track of where a feature lives across branches/worktrees/instances @simonw.
  • Recovery trick: after hacking in /tmp and crashing, he recovered the code from ~/.claude/projects/ session logs, and Claude Code could extract and recreate the missing feature @simonw.

• Turn your feedback firehose into PRs (fast iteration loop)

  • Cherny’s pattern: point Quad/Cowork at an internal Slack feedback thread; it proposes changes and opens PRs quickly, which encourages more feedback because users feel heard Lenny's Podcast.
  • Bug-fix loop: “as long as the description is good,” he can fix a bug in minutes by delegating to Claude Lenny's Podcast.

• Token policy as a productivity lever (especially early)

  • Cherny recommends giving engineers as many tokens as possible early (even “unlimited tokens” as a perk) so they try ideas that would otherwise feel too expensive; optimize/cost-cut after an idea works Lenny's Podcast.

• Avoid over-orchestration: tools + goal > rigid workflows (model-first design principle)

  • Cherny: don’t “box the model in” with strict step-by-step workflows; give it tools + a goal and let it figure it out—he argues heavy scaffolding mattered a year ago but often isn’t necessary now Lenny's Podcast.

• “Ephemeral app” mindset + AI-native interfaces (Karpathy)

  • Karpathy built a one-off cardio experiment dashboard with Claude; it had to reverse engineer a treadmill cloud API, process/debug data, and build a web UI; he still had to chase bugs (units, calendar alignment) @karpathy.
  • His takeaway: the app-store model feels outdated for long-tail needs; instead, the industry needs AI-native sensors/actuators with agent-friendly APIs/CLIs so agents don’t have to click HTML UIs or reverse engineer services @karpathy.

• Agent “memory” ops in practice (LangSmith Agent Builder)

  • LangChain’s concrete guidance:
    • Tell your agent to remember what works@LangChain
    • Use skills to inject specialized context when needed @LangChain
    • Edit agent instructions directly when it’s faster @LangChain
  • Entry point: https://blog.langchain.com/how-to-use-memory-in-agent-builder/?utm_medium=social&utm_source=twitter&utm_campaign=q1-2026_ab-philosophy_aw@LangChain.

👤 PEOPLE TO WATCH

• Boris Cherny — production-grade Claude Code habits (plan mode, parallel sessions) + strong claims about where “after coding” goes Lenny's Podcast.
• Andrej Karpathy — high-signal framing: ephemeral bespoke apps + “AI-native CLI/API” requirements for tools and hardware vendors @karpathy.
• Simon Willison — the best micro-case study of parallel-agent failure/recovery using session logs as the source of truth @simonw +1.
• Steve Ruiz (tldraw) — pragmatic company-building: code gets easier, but alignment/positioning/communication get harder—and he’s automating the overhead away Changelog.
• Theo — sharp practitioner critique on Claude Code regressions plus continued pressure on “harness vs infra” policy differences across vendors @theo +1.
• François Chollet — frames agentic coding as ML optimization (spec/tests as constraints) and asks what the “Keras of agentic coding” will be @fchollet; @swyx suggests DSPy as the presumptive community default @swyx.

🎬 WATCH & LISTEN

1) Boris Cherny — “Plan mode” as the default starter move (~1:09:52–1:10:41)

Hook: a simple, copyable workflow: force planning first (no code), iterate the plan, then execute + auto-accept when the plan is solid Lenny's Podcast.

2) Boris Cherny — “Coding is largely solved… what’s next?” (~0:18:19–0:19:06)

Hook: his thesis on why the frontier is shifting from IDE coding to adjacent operational tasks and general automation Lenny's Podcast.

3) Steve Ruiz — daily automated release notes from landed PRs (~0:20:35–0:21:02)

Hook: treat agents like scheduled staff: every day, Claude scans the last 24h PRs and drafts “release notes we’d publish if we shipped main today” Changelog.

📊 PROJECTS & REPOS

• NanoClaw — “Clawdbot” in ~500–700 LOC TypeScript using Apple container isolation for sandboxing/security; posted as Show HN @betterhn20 +1. Repo: https://github.com/gavrielc/nanoclaw@betterhn20 • HN: https://news.ycombinator.com/item?id=46850205@betterhn20.
• Nullclaw — “fastest, smallest OpenClaw clone”: 678 KB static binary, no runtime/VM/framework overhead @jedisct1. Repo: https://github.com/nullclaw/nullclaw@jedisct1.
• tldraw agent starter kit — Cursor-like agent panel next to a canvas; cloneable starter for agent+canvas UX: https://tldraw.dev/starter-kits/agentChangelog.

Editorial take: As agents make code cheap, the new edge is orchestration discipline: plan-first loops, sandboxing, session-log recoverability, and AI-native interfaces that don’t force your agent to “be the computer.” Lenny's Podcast +3

Top Stories

1) Google ships Gemini 3.1 Pro, pushing reasoning + cost efficiency

Why it matters: This is a broad distribution release (consumer, developer, enterprise) paired with third-party benchmarking that emphasizes a price/performance edge—an increasingly decisive axis as models converge.

Google announced Gemini 3.1 Pro as the “core intelligence” behind Gemini 3 Deep Think, now scaled for practical applications @Google +1. Google positions it as a new baseline for complex problem-solving, citing 77.1% on ARC-AGI-2 (novel logic patterns), described as more than double Gemini 3 Pro @Google.

Independent benchmarking from Artificial Analysis reports Gemini 3.1 Pro Preview as the top model on its Intelligence Index, with a notable advantage in price and token efficiency: <$50% evaluation cost versus Claude Opus 4.6 (max) and GPT-5.2 (xhigh) @ArtificialAnlys. Artificial Analysis lists pricing at $2/$12 per 1M input/output tokens for Gemini 3.1 Pro Preview, with total eval cost $892 (vs $2,304 for GPT-5.2 xhigh and $2,486 for Opus 4.6 max) @ArtificialAnlys.

They also report reduced hallucination behavior on AA-Omniscience: hallucination rate reduced from 88% to 50% (and +17 Omniscience Index points) @ArtificialAnlys.

2) Gemini 3.1 Pro lands across major dev surfaces (and some tooling frictions show up)

Why it matters: “Model quality” only translates to user impact when it’s reachable in the tools people already use—and reliability of those surfaces can quickly dominate perception.

Rollout/availability highlights include:

• Gemini app + NotebookLM (consumers) and Vertex AI / Gemini Enterprise (enterprise) @Google
• Developers via preview in Gemini API / Google AI Studio@Google +1
• GitHub Copilot public preview; GitHub reports early testing shows high tool precision and efficient edit-then-test loops @github
• Perplexity upgraded Gemini 3 Pro → Gemini 3.1 Pro for all Pro/Max users (consumer + enterprise) @AravSrinivas +1
• OpenRouter availability (preview) @scaling01 +1

At the same time, some early users report friction in Google’s coding toolchain: Gemini CLI not showing Gemini 3.1 Pro after installation, and Antigravity issues including failing requests and confusing model attribution (e.g., selecting Gemini 3.1 Pro (High) but being told it’s “powered by Claude 3.7 Sonnet”) @Yuchenj_UW +1.

3) OpenAI expands enterprise + national footprint: India partnership, FedRAMP authorization, and usage growth signals

Why it matters: The combination of (1) large-scale partnerships, (2) compliance milestones, and (3) steep usage growth points to continued acceleration in production adoption.

OpenAI announced an “OpenAI for India” initiative, partnering with Tata Group to build “sovereign AI infrastructure,” drive enterprise transformation with the Tata ecosystem, and partner with institutions to advance education @snsf.

Separately, OpenAI is now FedRAMP 20x Low authorized (per an announcement linking to the FedRAMP marketplace listing) @cryps1s.

On usage, OpenAI shared metrics cited in posts:

• ChatGPT message volume grew 8× YoY@scaling01
• API “reasoning token consumption per organization” increased 320× YoY@scaling01
• “More than 9,000 organizations” processed >10B tokens, and nearly 200 exceeded 1T tokens@scaling01

4) Mistral releases Voxtral Realtime (open) for low-latency transcription

Why it matters: Open licensing plus sub-second latency is a practical combo for real-time voice products, where deployment constraints and responsiveness matter as much as raw accuracy.

Mistral released Voxtral Realtime, stating it achieves state-of-the-art transcription at sub-500ms latency and is released under Apache 2@GuillaumeLample. They also shared a technical report, model weights, and a playground @GuillaumeLample.

5) Specialized inference hardware bets: “the chip is the model”

Why it matters: With growing concerns about inference scarcity, approaches that hard-specialize silicon to a given model aim to dramatically reshape latency/cost tradeoffs.

Awni Hannun highlighted Taalas running Llama 3 8B at 16k tokens/s per user, describing the key idea as: “each chip is specialized to a given model. The chip is the model.” @awnihannun

Research & Innovation

Why it matters: This cycle’s research emphasizes agent realism (memory across sessions, tool use), faster generation paradigms (diffusion LM latency), and methods to make long context usable.

Agent memory: benchmarks that test use, not recall

New research introduces MemoryArena, a benchmark evaluating memory across interdependent multi-session tasks where agents must learn from prior interactions and apply knowledge later @dair_ai. The authors argue existing long-context memory benchmarks (e.g., LoCoMo) are misleading: high recall doesn’t ensure correct multi-session actions, and models that saturate those benchmarks can perform poorly in “real agentic scenarios” @dair_ai. Paper: https://arxiv.org/abs/2602.16313@dair_ai.

Iterative reasoning with summaries: InftyThink+

Researchers from Zhejiang University and Ant Group presented InftyThink+, which trains models to think → summarize → continue in loops, optimized with trajectory-level RL @TheTuringPost +1. Reported gains include +21% accuracy on AIME24, 32.8% lower latency, and 18.2% faster RL training@TheTuringPost. Paper: https://arxiv.org/abs/2602.06960@TheTuringPost.

Faster diffusion LMs via post-training: CDLM

Together Research introduced Consistency Diffusion Language Models (CDLM), a post-training recipe for block-diffusion models targeting KV-cache incompatibility and high step counts @togethercompute. On Dream-7B, they report 4.1–7.7× fewer refinement steps and up to 14.5× lower latency with competitive math/coding accuracy @togethercompute.

Context compaction: Attention Matching (AM)

A new approach called Attention Matching (AM) proposes fast, high-quality context compaction in latent space, reporting 50× compaction in seconds with little performance loss vs summarization baselines @AdamZweiger.

Search/retrieval models: ColBERT-Zero

Researchers introduced ColBERT-Zero, a multi-vector model trained without distillation on top of dense models, claiming a new SOTA on BEIR using only public data @antoine_chaffin.

Safety in self-evolving agent societies: “self-evolution trilemma”

Researchers described a “self-evolution trilemma” for agent societies: you can’t simultaneously have continuous self-evolution, isolation, and stable safety alignment@TheTuringPost. They outline failure modes (consensus hallucinations, alignment drift, communication collapse) and mitigation ideas like external verifiers and checkpointing/rollback @TheTuringPost +1. Paper: https://arxiv.org/abs/2602.09877@TheTuringPost.

Products & Launches

Why it matters: The most durable gains come from shipping: models into workflows, tooling that reduces friction, and “agent ops” features that make systems observable and controllable.

Gemini 3.1 Pro: capability demos + access points

Google showcased Gemini 3.1 Pro building:

• A real-time ISS tracking dashboard combining public API telemetry, responsive UI, and physics-based day/night cycles @Google
• Website-ready animated SVGs generated from text prompts (pure code; crisp at any scale) @Google
• A 3D starling “murmuration” simulation reacting to hand-tracking with a generative score @Google
• A city planner app that tackles terrain, infrastructure mapping, and traffic simulation for visualization @GoogleDeepMind

Access points highlighted across announcements include Gemini App/NotebookLM for consumers and AI Studio/Gemini API for developers @sundarpichai.

ChatGPT: more interactive Code Blocks

OpenAI announced that Code Blocks in ChatGPT are “more interactive,” supporting writing/editing/previewing code in one place and previews for diagrams/mini apps (split-screen and full-screen views) @OpenAIDevs. They also called out previews for Mermaid flowcharts and debugging snippets @OpenAIDevs +1.

Claude in PowerPoint

Anthropic’s Claude in PowerPoint is now available on the Pro plan, and supports connectors to bring context from daily tools into slides @claudeai. Try it: https://claude.com/claude-in-powerpoint@claudeai.

W&B: Serverless SFT (public preview)

Weights & Biases launched Serverless SFT in public preview, with managed infrastructure powered by CoreWeave and features like training LoRAs and auto-deploying checkpoints; adapter training is free during preview@wandb +1.

Agent operations: tracing, filtering, and “agent trace search”

• Raindrop AI announced Trajectory Explorer, making agent decisions searchable “in seconds,” with emphasis on finding expensive or error-prone tool calls across traces @benhylak +1.
• LangSmith improved trace filtering UX (easier apply/edit; active filters visible at a glance) @LangChain.

Cursor: agent sandboxing on desktop OSes

Cursor rolled out agent sandboxing across macOS, Linux, and Windows; agents run freely inside a sandbox and request approval to step outside it @cursor_ai.

Industry Moves

Why it matters: Partnerships, capital, and distribution define which systems become defaults—especially for agents, where reliability and ops maturity are major differentiators.

Agent reliability and orchestration funding: Temporal

Temporal raised $300M Series D at a $5B valuation (led by a16z) to scale its open-source platform focused on making AI agents fault-tolerant by logging actions and enabling recovery from failures @dl_weekly.

Airtable announces Hyperagent

Airtable launched Hyperagent, positioning it as an agents platform where each session gets an isolated cloud compute environment (browser, code execution, image/video generation, data warehouse access, integrations, and skill learning for new APIs) @howietl. It also includes one-click Slack deployment and a “command center” to oversee fleets of agents @howietl.

Anthropic vs OpenAI revenue trajectory (Epoch AI)

Epoch AI Research reported that since each hit $1B annualized revenue, Anthropic has grown faster (10× vs OpenAI’s 3.4× per year) and “could overtake OpenAI by mid-2026” if trends continued @EpochAIResearch. Epoch notes extrapolations are aggressive and expects slowing; it also states Anthropic growth may have slowed to 7×/year since July 2025 @EpochAIResearch +1.

Model hosting + distribution signals

• Baseten announced GLM 5 live on its platform, positioning it around long-horizon agentic capabilities and tool calling for “real life” work use cases @basetenco.
• SambaNova promoted MiniMax M2.5 on SambaCloud for productivity agents, citing 80.2% SWE-Bench and 300+ t/s, with enterprise tier available now @SambaNovaAI.

Policy & Regulation

Why it matters: Compliance milestones unlock sensitive deployments; government actions can throttle or accelerate autonomy adoption.

OpenAI: FedRAMP authorization

OpenAI has achieved FedRAMP 20x Low authorization, with a link to the FedRAMP marketplace listing @cryps1s.

Autonomous vehicles: New York pauses robotaxi expansion

TechCrunch reported that New York hit the brakes on a robotaxi expansion plan@TechCrunch.

India: Google’s AI Impact Summit updates

At the AI Impact Summit in India, Google announced several accessibility and safety-related AI updates, including a live speech-to-speech translation model (real-time conversations in 70+ languages) and noting SynthID verification usage “over 20 million times” since November @Google.

Quick Takes

Why it matters: Smaller launches and sharp observations often foreshadow where the next wave of engineering effort is going.

• Tool calling risk: Researchers warned that some LLMs may request calling tools that were not provided in the allowed list—raising access-control concerns; one post claims this impacts major US providers except OpenAI @jeremyphoward.
• Embeddings: Jina released jina-embeddings-v5-text with small (677M) and nano (239M) variants, including a decoder-only + last-token pooling design and multiple LoRA adapters selectable at inference @JinaAI_ +1.
• Real-time speech: Voxtral Realtime resources include the arXiv report and HF weights @GuillaumeLample.
• ChatGPT growth: Technology sector seen as “over 10×” YoY growth (per one post) @scaling01.
• Benchmarking agents: Official SWE-bench leaderboard updated using the same scaffold (mini-SWE-agent v2) with cost analysis and trajectories @KLieret.
• Anthropic political spend: QuiverQuant reported Anthropic put $20M into a super PAC supporting candidates favoring more extensive AI regulation @QuiverQuant.
• Human detection limits: A study report said participants (including “super-recognisers”) performed barely better than chance at spotting AI-generated faces, despite high confidence @kimmonismus.
• Compute as a productivity constraint: Candidates are increasingly asked about dedicated inference compute for Codex, with usage per user growing faster than user count—suggesting scarcity @thsottiaux.
• Prompt caching: A guide describes prompt caching as a “most bang for buck” optimization for agent workflows, and Anthropic added automatic prompt caching to its API so devs don’t set cache points manually @dejavucoder +1.
• Gemini on ARC-AGI-3 harness: A reported config bug initially called Gemini 3.0 Pro instead of 3.1; after fixes, Gemini 3.1 Pro showed “much better performance” and could solve some games @scaling01.

Most compelling recommendation (highest operator leverage)

Paul Graham — “Founder Mode” (essay)

• Title: “Founder Mode” The Peel with Turner Novak
• Content type: Essay The Peel with Turner Novak
• Author/creator: Paul Graham The Peel with Turner Novak
• Link/URL: Not provided in the source excerpts
• Who recommended it: Garry Tan (Y Combinator President & CEO) The Peel with Turner Novak
• Key takeaway (as shared): Post–product-market fit, “leadership is…presence, not absence,” and the common advice to hire great people and “give them the keys to [the] kingdom” can backfire—creating “a company that I did not recognize.” The Peel with Turner Novak
• Why it matters: It’s a direct, experience-backed prompt for founders to stay actively involved as the company scales, especially after PMF when delegation patterns harden. The Peel with Turner Novak

Companion listen (same theme):

Social Radars — episode with Brian Chesky & Jessica Livingston on “Founder Mode” (podcast episode)

• Title: Social Radars episode on Founder Mode (Brian Chesky with Jessica Livingston) The Peel with Turner Novak
• Content type: Podcast episode The Peel with Turner Novak
• Author/creator: Social Radars (episode featuring Brian Chesky and Jessica Livingston) The Peel with Turner Novak
• Link/URL: Not provided in the source excerpts
• Who recommended it: Garry Tan The Peel with Turner Novak
• Key takeaway (as shared): Tan points to Chesky’s reflections (including remaking Airbnb during COVID) as a practical complement to the “Founder Mode” idea. The Peel with Turner Novak
• Why it matters: A founder-case narrative that Tan found instructive alongside the essay. The Peel with Turner Novak

AI & building: principles + mental models

Richard Sutton — “The Bitter Lesson” (blog post)

• Title: “The Bitter Lesson” Lenny's Podcast
• Content type: Blog post Lenny's Podcast
• Author/creator: Richard Sutton Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny (Head of Claude Code at Anthropic) Lenny's Podcast
• Key takeaway (as shared): “The more general model will always outperform the more specific model.” Lenny's Podcast
• Why it matters: A concise heuristic for AI product and research strategy—prioritizing general approaches over narrow, hand-crafted specialization. Lenny's Podcast

“Keep your identity small” (essay/blog post)

• Title: Essay/blog post advising “keep your identity small” No Priors: AI, Machine Learning…
• Content type: Blog post / essay No Priors: AI, Machine Learning…
• Author/creator: A founder from Applied Intuition (name not specified) No Priors: AI, Machine Learning…
• Link/URL: Not provided in the source excerpts
• Who recommended it: Speaker A (in a conversation featuring Elad Gil) No Priors: AI, Machine Learning…
• Key takeaway (as shared): “Keep your identity small,” described as “wonderful overall advice for this period of time.” No Priors: AI, Machine Learning…
• Why it matters: A direct reminder to stay adaptable—avoid over-anchoring to a fixed self-concept while circumstances shift. No Priors: AI, Machine Learning…

Deep work: one technical book pick

Functional Programming in Scala (book)

• Title: Functional Programming in ScalaLenny's Podcast
• Content type: Book Lenny's Podcast
• Author/creator: Not specified in the source excerpts
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Called “the single best technical book” he’s read; highlights “elegance” in functional programming and “thinking in types,” and frames it as something that can “level you up.” Lenny's Podcast
• Why it matters: Recommended as a durable way to improve how you think about code—not just a language-specific manual. Lenny's Podcast

Fiction that shaped leaders’ thinking (and taste)

J.R.R. Tolkien — The Lord of the Rings (book)

• Title: The Lord of the RingsThe Generalist
• Content type: Book The Generalist
• Author/creator: J.R.R. Tolkien The Generalist
• Link/URL (as shared): https://www.amazon.com/Lord-Rings-J-R-R-Tolkien/dp/0544003411The Generalist
• Who recommended it: Markus Villig (Founder & CEO, Bolt) The Generalist
• Key takeaway (as shared): Villig credits it with shaping his worldview. The Generalist
• Why it matters: A rare, explicit “this shaped how I see the world” endorsement from a founder. The Generalist

“Strauss” — Accelerando (book)

• Title: AccelerandoLenny's Podcast
• Content type: Book Lenny's Podcast
• Author/creator: “Strauss” (as stated in the excerpt) Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Recommended for capturing “the essence of this moment” through its accelerating pace. Lenny's Podcast
• Why it matters: A narrative frame for understanding rapid technological change—recommended specifically for its sense of speed and escalation. Lenny's Podcast

Cixin Liu — The Wandering Earth (short story collection)

• Title: The Wandering EarthLenny's Podcast
• Content type: Short story collection Lenny's Podcast
• Author/creator: Cixin Liu Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Highlighted for “amazing stories” and for offering a “different perspective than Western sci fi.” Lenny's Podcast
• Why it matters: A specific pointer to broaden creative inputs—both stylistically and culturally—via sci-fi with a distinct viewpoint. Lenny's Podcast

Business & crypto: two listens worth queuing

Acquired — start with the Nintendo episode (podcast)

• Title: Acquired (podcast) Lenny's Podcast
• Content type: Podcast Lenny's Podcast
• Author/creator: Ben and David (as stated in the excerpt) Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Praised for making “business history…alive,” with a specific suggestion to begin with a Nintendo episode. Lenny's Podcast
• Why it matters: A concrete entry point into a long-running show—recommended for clarity and storytelling around business history. Lenny's Podcast

@programmer at EthereumDenver — talk on the x402 protocol for agentic payments (video)

• Title: Talk on the x402 protocol for agentic payments @brian_armstrong
• Content type: Video talk @brian_armstrong
• Author/creator: @programmer @CoinbaseDev
• Link/URL (as shared): https://www.youtube.com/watch?v=MeTEQ4pHv3U@CoinbaseDev
• Who recommended it: Brian Armstrong (Coinbase CEO) @brian_armstrong
• Key takeaway (as shared): Armstrong calls it a “Good talk” on x402 for agentic payments. @brian_armstrong
• Why it matters: A direct founder endorsement of a concrete technical talk in the “agentic payments” space. @brian_armstrong

Big Ideas

1) Customer feedback has maturity phases—and teams need to upgrade their approach as they scale

Bir Khan (VP Product at Enterpret) shared a maturity framework that maps how teams evolve in customer feedback handling as org size and feedback volume grow Product School.

• Phase 1 (1–3 PMs): Intuition works; you can read most feedback (a few hundred items) and use simple tools like spreadsheets and quick AI summaries Product School.
• Phase 2 (5–6 PMs, ~2k–5k feedback/year): Bias creeps in (over-relying on power users), LLMs struggle with context, and manual tagging becomes a recurring tax; insights arrive in batches—often after decisions are already made Product School.
• Phase 3 (6+ PMs, >5k feedback/year): Manual workflows break; “volume-based” prioritization fails across segments (e.g., a few enterprise requests can matter more than many low-revenue requests); teams need a shared view of customer reality linked to revenue/retention/satisfaction metrics Product School.

Why it matters: If your tooling stays “Phase 1/2” while your org moves into Phase 3, you’ll feel it as slower planning cycles, more manual work, and leaders becoming disconnected from what customers are saying Product School.

How to apply: Use the phase descriptions as a diagnostic in your next planning retro: identify your current phase and explicitly decide what you’ll stop doing manually (e.g., tagging) vs. what must become continuously updated intelligence Product School.

2) Some “growth problems” are product problems in disguise

Andrew Chen’s heuristic from “20+ years working in startups”: most founders don’t have a growth problem—they have a product problem disguised as a growth problem@andrewchen.

Why it matters: It’s a reminder to pressure-test the underlying product value (and user reality) before defaulting to acquisition tactics.

How to apply: When growth stalls, run a quick product-reality check: what are the top recurring pain points, which are tied to high-value segments, and what’s the measurable impact of fixing them (see the tactical dashboards below) Product School.

3) In agentic AI, the “breakthrough” is often the interaction model—not raw capability

Sachin Rekhi frames a shift in AI tooling as a change in how tools interact with users:

• Claude Code: “controlled power” via confirmations, scoped access, and visible actions @sachinrekhi.
• OpenClaw: “frictionless autonomy,” with full system access and multi-step execution without approvals—bringing risks like unintended destructive actions, prompt injection with real consequences, silent data exposure, and zero audit trail @sachinrekhi.

He draws a parallel to Zoom: removing friction (no accounts, one-click links, simple setup) drove adoption @sachinrekhi, but also enabled Zoom-bombing—because some “friction” was actually security @sachinrekhi.

Why it matters: PMs designing AI agents will win or lose on where they place checkpoints (approvals, scope limits, audit logs)—not just on model performance @sachinrekhi.

How to apply: Treat “friction” as a design lever: remove it from onboarding and obvious flows, but add it back exactly at high-risk actions (data access, destructive commands, external communications) @sachinrekhi.

4) Roles are blurring: “everyone codes,” and generalists may be rewarded

In a conversation about Claude Code, Boris Cherny describes teams where PMs, designers, and others code, and argues many of the most effective people “cross over disciplines” Lenny's Podcast. He also predicts roles may get “murkier,” to the point where “software engineer” gets replaced by “builder,” with “everyone’s going to be a product manager and everyone codes” Lenny's Podcast.

Why it matters: If your org is adopting AI coding tools, the competitive advantage may shift toward hybrid builders who can connect product, design, business, and user context—not just write code Lenny's Podcast.

How to apply: Encourage (and reward) cross-functional “small loop” work: rapid prototyping, direct user contact, and shipping plus measurement—especially in early exploration Lenny's Podcast.

Tactical Playbook

1) Planning: prioritize feedback by business impact, not just volume

Enterpret’s guidance: traditional feedback analysis overweights counts (tickets/requests) and can be misleading; instead, quantify feedback with business context—revenue impact, NPS impact, and customer segment fit Product School.

Step-by-step:

1. Define your weighting dimensions: revenue impact, NPS/satisfaction impact, and segment/ICP fit Product School.
2. Connect data sources: integrate CRM/customer revenue data into your feedback system so requests can be ranked by revenue influence, not just number of mentions Product School.
3. Build a planning dashboard: rank feature requests and complaints by revenue/retention/satisfaction potential (vs. “loudest voice wins”) Product School.
4. Avoid “stale tagging” at scale: if manual tagging can’t keep up with tens of thousands of records, treat that as a system constraint (not a people problem) Product School.

2) Scoping: use AI to accelerate discovery—but don’t skip direct user research

Khan’s stance is explicit: “Nothing beats talking to users directly”—AI doesn’t replace it Product School. But AI can act as a force multiplier to validate hypotheses quickly and identify the right users to talk to Product School.

Step-by-step:

1. Write your hypothesis and ask: “What are the top pain points users mentioned about [flow]?” to get an answer grounded in your existing feedback corpus Product School.
2. Ask for targeting lists: “Who requested [integration] over the last six months?” so you can reach out for interviews Product School.
3. For alpha/beta recruiting: ask for customers who complained about the current capability—they’re strong early-access candidates Product School.
4. Before key customer meetings, ask for a short brief: top issues, past feature requests, and sentiment over time to reduce prep time Product School.

3) Post-launch: make “before/after” dashboards a habit (so you can prove impact)

Khan argues many teams “ship the feature, celebrate and move on,” without building the muscle to learn from launches Product School. The fix: launch-specific dashboards and before/after analysis to quantify outcomes Product School.

Step-by-step:

1. Define the pre-launch baseline (tickets, request volume, revenue impact, sentiment) Product School.
2. Ship.
3. Track the post-launch delta and package it for leadership.
4. Use a concrete narrative: “We were getting X complaints, shipped the fix, now we’re getting Y—here’s customer impact” Product School.

Example metric: a login bug fix reduced support tickets from 2,000/week to 200/week (90% reduction)Product School.

4) Stakeholder management: prevent “proposals” from silently turning into commitments

A recurring pitfall from a leadership-meeting pattern: a proposal floated under urgency shifts from “what if we just…” to “okay, so we’re doing this,” and soon it’s on a slide as a commitment r/prodmgmt. Even with guardrails like “This isn’t commitment” and “We need to validate first,” “9 times out of 10, the first proposal becomes the plan” r/prodmgmt.

Step-by-step (lightweight meeting guardrail):

1. Label agenda items explicitly as Explore / Decide / Commit (don’t rely on verbal caveats alone) r/prodmgmt.
2. Require a named validation step when someone says “we need to validate first” (owner + what “validated” means + when you’ll revisit) r/prodmgmt.
3. If you must set timing, separate “date for the date” (decision date) from delivery commitments r/prodmgmt.

5) Designing agent friction: relocate checkpoints instead of removing them

Rekhi’s punchline: “Friction isn’t the enemy. Badly placed friction is.” Winners won’t choose between speed and safety—they’ll redesign where friction lives so users get both @sachinrekhi.

Step-by-step:

1. List the agent’s high-risk actions (data access, deletions, sending messages, system changes) @sachinrekhi.
2. Decide which require confirmations and scoped access, and ensure visible actions and an audit trail in UX @sachinrekhi.
3. Remove friction elsewhere (setup, joining, onboarding), but treat security friction as intentional, not accidental @sachinrekhi.

Case Studies & Lessons

1) Claude Code: a “build for the model six months from now” bet + adoption metrics

Lenny shared that Claude Code launched one year ago and “today it writes 4% of all GitHub commits, and DAU 2x’d last month” @lennysan.

A core product bet (from Cherny):

Takeaways for PMs:

• If your product is gated by model capability, your roadmap may need to anticipate near-term model shifts @lennysan.
• Track adoption with concrete leading indicators (e.g., commit share, DAU acceleration) @lennysan.

2) Cowork: spotting latent demand from “misuse,” then building a safer, more accessible product

Cherny describes a “latent demand” pattern: users “abuse” a product to do things it wasn’t designed for—and that teaches you where to take it next Lenny's Podcast.

In Claude Code, they saw extensive non-coding use cases (e.g., growing tomato plants, genome analysis, recovering corrupted photos) and people “jumping through hoops to use a terminal” Lenny's Podcast. That became a signal to build something purpose-built Lenny's Podcast.

Implementation detail: Cowork was created by putting Claude Code into the desktop app; it included “a very sophisticated security system” with guardrails (including shipping a virtual machine), was built in ~10 days (entirely with Claude Code), and launched early while still “rough around the edges” to learn from feedback Lenny's Podcast.

Takeaways:

• Misuse is a discovery channel: watch for repeated “workarounds” that indicate demand Lenny's Podcast.
• Accessibility often requires safety/guardrails, not just a new surface area Lenny's Podcast.

3) ShowMe: multi-agent AI sales reps, staged rollout, and visibility as a trust mechanism

ShowMe’s origin: lots of website visitors weren’t converting; putting a human sales rep in front of visitors improved conversion, but it was too costly for unqualified leads—AI could filter and route accordingly Product Talk. They also found a free trial motion didn’t work due to a complex product and late “aha moment” Product Talk.

What they built: AI “digital workers” that behave like teammates—sales skills (voice/text/video calls, screen share, demos, phone calls, messaging) plus operational skills (reporting via Slack/email, sharing metrics) Product Talk.

How it evolved:

• MVP in ~2 weeks: voice agent narrating selected product videos with Q&A; used by a first external customer despite clunkiness Product Talk.
• Expanded to support different buyer stages (e.g., pricing, customer stories) and added a realistic avatar (HeyGen) and a Zoom-like UI because users were already trained on video-call affordances—and talked more to the AI “as a human” Product Talk.
• Split complex conversations into multiple agents (greetings/discovery, qualifying, pitching) due to model limitations and voice-latency constraints Product Talk.

Quality + rollout loop:

• Start with a POC, validate what the customer is trying to prove, and roll out via A/B tests (often starting with lower-quality leads) before full rollout over 1–2 months Product Talk.
• Build customer confidence via visibility: share conversations in Slack, log into CRM, and provide dashboards so customers can see interaction quality—not just be told it’s good Product Talk.
• Use customer reviews to generate tests and rerun conversations until passing, building a growing “battery” of tests to preserve improvements over time Product Talk.

4) Zoom: removing friction drove adoption—then security had to catch up

Zoom’s advantage versus Webex was removing friction (no accounts, one-click links, simpler setup, fast joins, effortless screen share), which drove adoption @sachinrekhi. But it also enabled Zoom-bombing, illustrating that some friction was actually security @sachinrekhi.

PM takeaway: Optimize friction placement, not friction elimination @sachinrekhi.

Career Corner

1) Become more of a generalist (and learn to cross boundaries)

Cherny’s advice: “try to be a generalist more than you have in the past,” noting that many effective engineers and PMs “cross over disciplines” (product + infra, product + design, business sense, user empathy) Lenny's Podcast. He describes a team dynamic where “everyone codes,” including PM, EM, designer, finance, and data science Lenny's Podcast.

How to apply: Pick one adjacent skill to deepen over a quarter (e.g., code enough to prototype, or do more user conversations) and use it to close a loop end-to-end on a small feature Lenny's Podcast.

2) Practical portfolio signal: owning outcomes across startup + enterprise contexts

A job seeker summary (ProductManagementJobs) highlights an arc many PMs will recognize:

• Founded a hyperlocal food delivery startup: managed a team of 20+, did 50+ customer interviews, built roadmap and GTM; learned from failure about product-market fit and unit economics r/ProductManagementJobs.
• Owned product frontend for an enterprise AI platform used by Fortune 500 supply chain teams, translating complex AI workflows into intuitive interfaces r/ProductManagementJobs.

How to apply: If you’re job searching, capture this as an “outcomes + learning” narrative: what you shipped, what changed, and what you learned from failure (explicitly called out as valuable) r/ProductManagementJobs.

Tools & Resources

1) An “AI PM tool stack” (one tool per category)

Aakash Gupta shared a tool stack used by top AI PMs, recommending you only need one tool per categoryAakash Gupta.

• Building
  • Vibe Coding: Cursor, Claude Code, Windsurf, Replit, Warp Aakash Gupta
  • Prototyping: Lovable, Bolt, v0, Magic Patterns, Base44 Aakash Gupta
• Productivity
  • Dictation: Wispr Flow, superwhisper, Tactiq, Speechify Aakash Gupta
  • Meetings: Granola, Fathom, Otter.ai, tl;dv, Fireflies Aakash Gupta
  • General LLMs: Claude, ChatGPT, Gemini, Kimi, Grok Aakash Gupta
• Automation
  • Simple Agents: Zapier, Lindy, Relay, Bardeen, Parabola Aakash Gupta
  • Full-Featured Agents: n8n, make, Activepieces, Workato, Tray Aakash Gupta
• Discovery
  • User Research: NotebookLM, Perplexity, Elicit, Consensus, Grain Aakash Gupta
  • Customer Intelligence: Dovetail, Unwrap, Enterpret, Monterey, Viable Aakash Gupta

2) Watch / read

• Product School (Enterpret VP Product): “Product Strategy Lessons from Notion, Stripe & Google” https://www.youtube.com/watch?v=AMUe4wBvNpwProduct School
• Lenny’s Podcast (ShowMe): “Building AI Sales Reps…” https://www.youtube.com/watch?v=5jMleOuL7SoProduct Talk
• Lenny’s Podcast (Boris Cherny / Claude Code): “Head of Claude Code…” https://www.youtube.com/watch?v=We7BZVKbCVwLenny's Podcast

3) Reading for stakeholder-meeting dynamics

• “How Chaos Gets Codified” (linked from the Reddit thread): https://notthoughtleadership.substack.com/p/how-chaos-gets-codified-when-proposalsr/prodmgmt

Market Movers

USDA 2026/27 outlook: fewer corn acres, more soybean acres

USDA’s 2026/27 outlook discussion across market sources converged on a corn acreage cut alongside a soybean acreage increase.

• Corn: projected around 94M planted acres with 183 bpa yield and about 1.837–1.84B carryout@MarketMinuteLLC +1
• Soybeans: projected around 85M planted acres, 53 bpa yield, and 355M carryout@MarketMinuteLLC +1
• Wheat: projected around 45M planted acres, 50.8 yield, and 933M carryout@MarketMinuteLLC +1

One summary of the release described corn acres “a little lower than expected” with carryout at 1.8, bean acres higher with carryout at 355, and wheat steady year-over-year at 933@GoddessofGrain.

Price action (Feb. 19): soybeans and wheat firmer; corn lagged

• Futures snapshot (Feb. 19): March corn $4.27¼; March soybeans $11.37¾; March Chicago wheat $5.53¾; March KC wheat $5.58¼; March spring wheat $5.77½Grain Markets and Other Stuff.

Market commentary tied wheat strength to short covering/technical buying, with additional support from production concerns in the U.S. and the Black Sea plus Middle East geopolitical risksFarm Journal.

Corn was described as struggling near chart resistance (430–435) into first notice day/delivery dynamics Farm Journal, while also being weighed down by a “too much of it” framing in one discussion of supply Farm Journal.

KC wheat: breakout + hedging signals

KC wheat was flagged at 8‑month highs and “trading to its highest levels since summer” @MarketMinuteLLC. Technical notes said KC wheat broke out of a multi‑year wedge and is on its longest sustained rally since 2021@MarketMinuteLLC, now pressing a key resistance level where a break “could spark further upside” @MarketMinuteLLC.

A producer-focused hedge alert noted KC wheat is nearing a target discussed “for months” Market Minute LLC and suggested, for those needing cash flow and unable to store, incremental sales or options protection (puts/calls)Market Minute LLC.

Innovation Spotlight

Brazil (Santa Catarina): open‑pollinated corn variety targets stability under drought

At the Itaipu Rural Show, Ipagri highlighted a new open‑pollinated corn variety (VPA) with lower seed cost, current yields around 160–165 sacas/ha, and potential up to 200 sacas/haCanal Rural. The variety’s longer flowering period was presented as a way to reduce losses during dry spellsCanal Rural.

Brazil: scaling ag-tech depends on shared connectivity models

ABDI described connectivity as a core bottleneck for “agriculture 4.0,” citing 70% of Brazil’s territory without connectivity coverage and highlighting shared infrastructure (e.g., cooperative antennas/satellite) as a practical model for smaller producers Canal Rural.

A Rio Grande do Sul example described a microclimate platform that aggregates multiple weather stations and offers access for about R$100/month, making data services cheaper for smallholders than buying their own stations Canal Rural.

Regenerative systems: new efforts to standardize definitions and measurement

A Spain-based effort set out 10 consensus-based scientific criteria for regenerative agriculture and cited findings that regenerative soils can sequester at least 35% more carbon and buffer summer ground temperatures by up to 3.6°C, while producing similar yields at similar or lower cost after transition This Week In Regenerative Agric….

Regional Developments

Brazil (Mato Grosso): harvest delays and quality loss from excess rain

In Paranatinga (MT), reports described persistent rain disrupting soybean harvest and raising uncertainty about losses Canal Rural. One producer cited 80 hectares ready for harvest but inaccessible due to moisture, with grain rotting and germinating; he estimated about 50% loss already and warned the field could be 100% lost if harvest couldn’t occur within 2–3 daysCanal Rural.

Conab progress updates said Brazil’s soybean harvest reached 24.7% of area but remained behind last year and the 5‑year averageCanal Rural.

Second‑crop corn planting was described as delayed by rain that slows soybean harvest (about 3% behind last year and nearly 6% behind the 5‑year average) Canal Rural. Mato Grosso was a bright spot at about 52.7% planted—9.4% ahead of last yearCanal Rural.

Brazil logistics: BR‑174 road conditions raise costs (Juína, MT)

Producers described BR‑174 (Juína–Vilhena corridor) as strategically important for flows toward Porto Velho Canal Rural, but reported severe road impacts—e.g., 11 km taking ~1 hour for a truck Canal Rural and tire damage costing R$6–7k on a short trip Canal Rural. Local basis impacts were also noted: soybean prices cited around R$92–93/sack in Juína vs R$100–103 in Campo Novo, with losses of about R$10/sack attributed to logistics Canal Rural.

Argentina: short strike halts grain shipments

A 48‑hour strike by Argentine maritime workers was reported as bringing grain shipments “to a standstill,” with the main oilseed crushers union planning a separate 24‑hour strike tied to proposed labor reforms Grain Markets and Other Stuff.

Black Sea: Ukraine winter wheat risk in the news flow

KC wheat strength was linked by some market commentary to Ukraine winter wheat risks following a thaw and then cold snap, according to Ukraine’s farmers union Grain Markets and Other Stuff.

Best Practices

Grain marketing: align hedges with storage and cash-flow constraints

• For producers unable to store wheat and needing cash flow, one hedge alert favored incremental sales or option structures (puts/calls or combinations) Market Minute LLC.
• For old-crop corn needing movement before spring, a separate recommendation emphasized downside protection, noting corn options were “so cheap it makes no sense” to be unprotected if movement is required soon Market Minute LLC.

Fertility placement: when banding can (and can’t) reduce fertilizer rates

Ag PhD highlighted interest in banding vs. broadcasting to reduce rates under high fertilizer prices, noting improved extraction and reduced tie-up when fertilizer is placed where roots will find it—especially for immobile nutrients like P, K, zinc, and copper@AgPhDMedia. It cautioned that banding offers less benefit for leachable nutrients (nitrate, sulfate, boron) and warned about salt injury risk near seed (lower rates and more distance are safer) @AgPhDMedia.

Livestock (Brazil): rainy-season supplementation targets measurable gain

A Brazil segment described how balancing energy, protein, and minerals during the rainy season can raise daily gains from about 450g up to 900g per animal/day, improving cash flow and shortening production cycles Canal Rural. A worked example described a 9‑arroba heifer reaching 12 arrobas in 90 days with energy supplementation (vs. about 6 months otherwise) Canal Rural.

Input Markets

Fertilizer remains a focal variable for 2026 row-crop decisions

One market discussion repeatedly emphasized fertilizer cash prices as a key variable in corn/soy acreage decisions, with observations of fertilizer movement “on fire” and farmers filling sheds later than usual (suggesting deferred fall buying) Farm Journal.

Agro policy also moved into the input narrative: President Donald Trump designated glyphosate-based herbicides and elemental phosphorus as critical to national defense, directing USDA to prioritize and secure domestic supplies @SuccessfulFarm.

Policy, demand, and crush: soybean oil for biofuel remains central

USDA’s outlook discussion included a projected record 17.3B lbs of soybean oil use for biofuel Farm Journal. Separately, USDA-ag-economy commentary framed the RFS/E15 debate and the 45Z rule (including flexible feedstock provisions) as “wildcards” that could affect corn/soy demand—and potentially the acreage and price outlook—depending on timing Farm Journal.

Forward Outlook

Watch-list: policy timing + trade headlines as near-term volatility sources

• USDA’s biofuel-policy framing explicitly highlighted the importance of the RFS/E15 pathway and the 45Z rule’s incentive structure as potential demand drivers for corn/soy/canola Farm Journal.

• Another market view tied recent soybean strength to improving U.S.–China relations and suggested markets may react to headlines in the lead-up to an April meeting; the same discussion recommended being “3/4 cash sold” on old-crop corn/soy/wheat ahead of that meeting window due to uncertainty Farm Journal.

Weather risk lens: KC winter wheat belt flagged for dry-spring and frost concerns

A weather-focused segment described a lag where sea-surface temperatures shifted toward neutral but the atmosphere remained “La Niña-like,” a pattern it said “always means a dry spring” for the KC winter wheat belt (KS/OK/NE), adding frost risk as wheat comes out of dormancy Farm Journal.

Gemini 3.1 Pro ships: benchmark jump + broad rollout

Gemini 3.1 Pro hits 77.1% on ARC-AGI-2

Google leaders say Gemini 3.1 Pro reaches 77.1% on ARC-AGI-2, described as more than 2× Gemini 3 Pro’s performance and a step forward in core reasoning @sundarpichai +2. DeepMind adds that ARC-AGI-2 tests novel logic patterns and that the model is aimed at workflows “where a simple answer isn’t enough” @GoogleDeepMind.

Why it matters: This is one of the clearest “headline” reasoning deltas in a mainstream model launch, and it immediately feeds into ongoing questions about what different evals actually capture (see “Evals” below). r/LocalLLM

Availability: Gemini App, NotebookLM, API preview, and enterprise

Google says Gemini 3.1 Pro is rolling out across multiple surfaces:

• Gemini App@GoogleDeepMind +1
• NotebookLM (exclusive to Google AI Pro/Ultra users) @GoogleDeepMind
• Developers via Gemini API preview in Google AI Studio@GoogleDeepMind +1
• Enterprises via Vertex AI and Gemini Enterprise@sundarpichai

Perplexity also upgraded Gemini 3 Pro → Gemini 3.1 Pro for all Pro/Max users (consumer and enterprise), and says it’s the second most picked model by its enterprise customers after the Claude 4.5 Sonnet/Opus family @AravSrinivas.

Why it matters: Distribution is not confined to one product—Google is pushing the same model into consumer, developer, and enterprise channels in parallel, with immediate third-party adoption. @sundarpichai +1

Demos: city planning, CAD → analysis, and SVG generation

Google and DeepMind showcased several “complex workflow” examples:

• A city planner app where the model handles complex terrain, maps infrastructure, simulates traffic, and produces visualizations @GoogleDeepMind. Jeff Dean also shared an urban planning simulation example for designing new cities @JeffDean.
• A “Deep Think” workflow (described as no tools, using Deep Think + image generation) that: generates a CAD file from a technical drawing, runs heat transfer analysis, and turns results into time-step visualizations@JeffDean +2.
• Improved SVG generation, including examples of prompt-to-SVG and follow-up edits @OriolVinyalsML +1. Another demo claims Gemini 3.1 Pro can generate web-ready animated SVGs from text prompts @addyosmani.

Why it matters: The messaging is less “chat answerer” and more “workflow engine”—including structured artifacts (CAD/SVG) and multi-step analysis/visualization. @JeffDean +1

Agentic engineering: post-IDE tools, compute constraints, and new security pitfalls

“Post-IDE” agent development environments (ADEs) keep solidifying

@swyx argues the shift to post-IDE agentic development environments is now “here,” pointing to Augment’s Intent as a consolidation of multiple code-agent management ideas (while not locking users into a single in-house agent) @swyx.

Why it matters: The competitive surface is moving from model quality alone to how agents are orchestrated and managed in day-to-day engineering workflows. @swyx

Agentic coding as “machine learning,” with ML-style failure modes

François Chollet frames sufficiently advanced agentic coding as essentially machine learning: engineers define an optimization goal + constraints (spec/tests), agents iterate, and the result is a black-box codebase often deployed without inspecting internal logic @fchollet. He warns classic ML issues will show up: overfitting to specs, “Clever Hans” shortcuts, data leakage, and concept drift @fchollet.

Why it matters: If codebases start to resemble trained artifacts, teams may need higher-level abstractions to steer “codebase training” and to manage reliability beyond conventional code review. @fchollet

Inference compute is becoming an explicit productivity bottleneck

Greg Brockman says the inference compute available to you will increasingly drive software productivity @gdb. He highlights an interview trend: candidates are being asked how much dedicated inference compute they will have for building with Codex, as usage per user grows faster than the user count—suggesting compute scarcity @gdb +1.

Why it matters: If teams treat inference capacity as a primary constraint, “agent throughput” could become a core planning variable alongside headcount and budgets. @thsottiaux

Tool-calling vulnerability: models may invoke tools you didn’t provide

Jeremy Howard points to a tool-calling issue where an LLM given a list of tools it’s allowed to call might decide to call a tool you didn’t provide@jeremyphoward. He says this impacts major labs (Anthropic, xAI, Gemini) and “all major US providers except OpenAI,” advising developers to check tool call requests@jeremyphoward.

Why it matters: As agents get more permissions, tool invocation becomes an access-control boundary—and failures here can turn “helpful automation” into unauthorized actions. @PiotrCzapla +1

Evals, governance, and “what’s actually happening in the world”

The eval mismatch shows up immediately: ARC-AGI-2 vs Arena (and “saturated” tests)

A LocalLLM post notes Gemini 3.1 Pro “just doubled its ARC-AGI-2 score,” while Arena still ranks Claude higher, calling it “exactly the AI eval problem” r/LocalLLM. Separately, a thread comments that a named eval was “saturated,” with criticism that lab leaders publicly tweeting about an eval implies it was (at minimum informally) targeted @HamelHusain +1.

Why it matters: Model comparisons are increasingly gated by which benchmark you trust—and by whether the evaluation itself stays robust under optimization pressure. r/LocalLLM +1

Government/standards groups push toward private testing + decision-linked benchmarks

From a panel on international evaluation practices, Sarah Hooker argues benchmarks are in a “muddy middle”: static, quickly overfit, and often gamified—supporting a return to private test sets and no-notice testing Diya TV. She also argues benchmarks should guide decisions—otherwise you’re “just collecting data” Diya TV.

Why it matters: As governments embed AI deeper into critical systems, evaluation regimes may shift toward private, operationally-relevant testing rather than public leaderboards. Diya TV +1

Anthropic scales its “Societal Impacts” team

Anthropic says it’s “aggressively scaling up” its Societal Impacts team as models begin having “non-trivial impacts on the world” @jackclarkSF. The team focuses on testing properties, building observation tools, and generalizing them across the org, including work supporting the Anthropic Economic Index and studying agents “in the wild” @jackclarkSF +1.

Why it matters: This is a sign that post-deployment measurement and feedback loops are becoming a first-class capability alongside model development. @jackclarkSF

India summit signals: competing timelines, diffusion focus, and coordination proposals

Altman: democratization, disruption, and an IAEA-like coordination concept

Sam Altman says OpenAI believes it may be “only a couple of years away” from early versions of true superintelligence, with the caveat they could be wrong; he adds that by end of 2028, more of the world’s intellectual capacity “could reside inside of data centers” than outside ANI News. He also calls for something “like the IAEA” for international coordination of AI with the ability to respond rapidly to changing circumstances CNBC-TV18.

Why it matters: This pairs aggressive capability timelines with explicit institutional proposals for cross-border coordination, reflecting how fast “governance architecture” is being pulled into mainstream leadership messaging. CNBC-TV18

Bengio: global, UN-rooted science-policy interface and “policy lag” risk

Yoshua Bengio argues AI capabilities are growing rapidly but unevenly, while scientific studies and policy processes create a lag that can become dangerous if things move too fast DRM News. He highlights the importance of a UN-rooted international panel and multidisciplinary work so “everyone is at the table and no one is on the menu” DRM News.

Why it matters: The emphasis is less on settling predictions and more on building mechanisms that can act under uncertainty—especially for high-severity risks. DRM News

Product + platform moves worth tracking

Microsoft adds xAI’s Grok 4.1 Fast to Copilot Studio

Microsoft says it’s adding xAI’s Grok 4.1 Fast to the multi-model lineup in Copilot Studio, positioning it as more choice/flexibility for building custom agents @satyanadella. Elon Musk also says Grok 4.20 is “coming soon” @elonmusk.

Why it matters: Multi-model “agent builders” are turning model choice into a platform feature—shifting competition toward orchestration, governance, and enterprise packaging. @satyanadella

Perplexity: Comet iOS pre-order + Finance auditability into SEC filings

Perplexity’s CEO says Comet (an iOS AI personal assistant/browser) is nearly ready and available for pre-order, aiming for a “Safari grade browser” with Perplexity powering each webpage and providing assistance @AravSrinivas. Separately, Perplexity Finance now includes tap-through auditability to SEC filings, pre-scrolled to the page where a cited line item appears @jeffgrimes9.

Why it matters: “Answer engines” are pushing deeper into verifiable workflows (finance audit trails) while also experimenting with new assistant-native browsing surfaces. @jeffgrimes9 +1

Research notes (fast scans)

Interpretability: “Cheap Anchor V2” predicts circuit edge importance from weights alone

A MachineLearning subreddit post reports “Cheap Anchor V2,” which predicts causal edge importance in GPT-2 small’s induction circuit using a composite of discrimination (spectral concentration) and cascade depth (downstream path weight) r/MachineLearning. It reports Spearman ρ=0.623 vs. path-patching ground truth with a 125× speedup (2s vs 250s), beating weight magnitude and gradient attribution baselines r/MachineLearning.

Why it matters: If reproducible, this suggests a cheaper pre-filter for mechanistic interpretability—scoring many candidate edges before spending expensive intervention compute. r/MachineLearning

Open neuromorphic processors: Catalyst N1/N2 claim Loihi parity + FPGA validation

A separate post introduces Catalyst N1 & N2, open neuromorphic processors aiming for feature parity with Intel Loihi generations, with N2 adding programmable neurons (five shipped models) and reporting FPGA integration tests with zero failures r/MachineLearning. It reports 85.9% SHD accuracy (float) and 85.4% (16-bit) r/MachineLearning.

Why it matters: This is a notable “open hardware + full stack” claim (papers, SDK, FPGA tests) in a space typically dominated by proprietary chips and platforms. r/MachineLearning

Small open-weight multilingual model: Tiny Aya (3.35B)

Sebastian Raschka highlights Tiny Aya (3.35B) from Cohere as a small open-weight model with strong multilingual support in its size class, suitable for on-device translation @rasbt. He calls out architectural choices like parallel transformer blocks, sliding window attention (4096 window; 3:1 local:global), and a modified LayerNorm without bias @rasbt.

Why it matters: Continued innovation in small-model architecture suggests the “open + on-device” track is still moving quickly alongside frontier scaling. @rasbt

🔥 TOP SIGNAL

Boris Cherny (Head of Claude Code) is blunt: for the kinds of programming he does, “coding is largely solved”, and the frontier is shifting to adjacent, end-to-end agentic work (project management, paying tickets, general ops) rather than better IDE autocomplete Lenny's Podcast. In that world, throughput isn’t hypothetical: he says Anthropic saw +200% productivity per engineer (PRs)Lenny's Podcast, and Claude now reviews 100% of pull requests (with human review still in the loop) Lenny's Podcast.

🛠️ TOOLS & MODELS

• Claude Code — stability + performance signals

  • v2.1.47: long-running sessions use less memory@jarredsumner.
  • Team guidance: keep reporting issues and they’ll fix them @jarredsumner.
  • Practitioner complaint: Theo reports Claude Code has “regressed an absurd amount” with UI/feedback issues (timestamps not updating, missing “thinking,” multi-minute hangs with 0 output) @theo and suggests it “needs to be rewritten from scratch” @theo.

• Cursor — agent sandboxing shipped across desktop OSes

  • Cursor says it rolled out agent sandboxing on macOS, Linux, and Windows over the last three months @cursor_ai.
  • Mechanism: agents run freely inside a sandbox, only requesting approval when they need to step outside it @cursor_ai.
  • Implementation write-up: http://cursor.com/blog/agent-sandboxing@cursor_ai.

• OpenAI Codex — pricing/availability + compute pressure

  • @thsottiaux: Codex is included with a ChatGPT subscription (even Plus has “very generous” usage) @thsottiaux; they attribute this to gpt-5.3-codex achieving “SoTA at lower cost” @thsottiaux.
  • Same source: candidates increasingly ask how much dedicated inference compute they’ll have, and usage/user is growing faster than user count → compute could be scarce@thsottiaux.

• Gemini 3.1 Pro — dev-workflow positioning (ramping up)

  • Google Antigravity: Gemini 3.1 Pro is ramping to Google AI Ultra/Pro users, pitched around “advanced reasoning” and “long horizon planning” for dev workflows @antigravity. Details: https://antigravity.google/blog/gemini-3-1-pro-in-google-antigravity@antigravity.

• GitHub Copilot → Zed editor (GA)

  • GitHub: Copilot subscription support in Zed is generally available @github. Changelog: https://github.blog/changelog/2026-02-19-github-copilot-support-in-zed-generally-available/@github.

• Model choice drift + self-hosting pressure (reported trend)

  • Salvatore Sanfilippo says he’s seeing excellent programmers move off US models (Codex, Claude Code) toward Chinese open-weight models like Kimi 2.5 and GLM5Salvatore Sanfilippo, often via providers or by building in-house Nvidia GPU inference to avoid outages and keep sensitive data internal Salvatore Sanfilippo.
  • He frames DeepSeek v4 as a potentially major moment if it lands as SOTA (as rumors suggest), putting pressure on OpenAI/Anthropic business sustainability Salvatore Sanfilippo.

💡 WORKFLOWS & TRICKS

• “Plan mode → execute” as a default loop (Claude Code / Boris Cherny)

  1. Start the task in plan mode (he says he does this for ~80% of tasks) Lenny's Podcast.
  2. Iterate on the plan (model goes back-and-forth) Lenny's Podcast.
  3. Once the plan is good, let it execute; he’ll auto-accept edits after that Lenny's Podcast.
  • Implementation detail: plan mode is literally a prompt injection: “please don’t write any code yet” Lenny's Podcast.

• Parallel agents, but treat “state” as a first-class problem

  • Cherny: he runs ~5 agents in parallel while working (terminal/desktop/iOS) Lenny's Podcast and highlights you can run many sessions in parallel Lenny's Podcast.
  • Kent C. Dodds: similar “utter chaos” workflow—multiple projects, “a couple cloud agents” each, plus a locally guided agent @kentcdodds.
  • Failure mode (real): Simon Willison describes “parallel agent psychosis”—losing track of where a feature lives across branches/worktrees/instances @simonw.
  • Recovery trick: after hacking in /tmp and crashing, he recovered the code from ~/.claude/projects/ session logs, and Claude Code could extract and recreate the missing feature @simonw.

• Turn your feedback firehose into PRs (fast iteration loop)

  • Cherny’s pattern: point Quad/Cowork at an internal Slack feedback thread; it proposes changes and opens PRs quickly, which encourages more feedback because users feel heard Lenny's Podcast.
  • Bug-fix loop: “as long as the description is good,” he can fix a bug in minutes by delegating to Claude Lenny's Podcast.

• Token policy as a productivity lever (especially early)

  • Cherny recommends giving engineers as many tokens as possible early (even “unlimited tokens” as a perk) so they try ideas that would otherwise feel too expensive; optimize/cost-cut after an idea works Lenny's Podcast.

• Avoid over-orchestration: tools + goal > rigid workflows (model-first design principle)

  • Cherny: don’t “box the model in” with strict step-by-step workflows; give it tools + a goal and let it figure it out—he argues heavy scaffolding mattered a year ago but often isn’t necessary now Lenny's Podcast.

• “Ephemeral app” mindset + AI-native interfaces (Karpathy)

  • Karpathy built a one-off cardio experiment dashboard with Claude; it had to reverse engineer a treadmill cloud API, process/debug data, and build a web UI; he still had to chase bugs (units, calendar alignment) @karpathy.
  • His takeaway: the app-store model feels outdated for long-tail needs; instead, the industry needs AI-native sensors/actuators with agent-friendly APIs/CLIs so agents don’t have to click HTML UIs or reverse engineer services @karpathy.

• Agent “memory” ops in practice (LangSmith Agent Builder)

  • LangChain’s concrete guidance:
    • Tell your agent to remember what works@LangChain
    • Use skills to inject specialized context when needed @LangChain
    • Edit agent instructions directly when it’s faster @LangChain
  • Entry point: https://blog.langchain.com/how-to-use-memory-in-agent-builder/?utm_medium=social&utm_source=twitter&utm_campaign=q1-2026_ab-philosophy_aw@LangChain.

👤 PEOPLE TO WATCH

• Boris Cherny — production-grade Claude Code habits (plan mode, parallel sessions) + strong claims about where “after coding” goes Lenny's Podcast.
• Andrej Karpathy — high-signal framing: ephemeral bespoke apps + “AI-native CLI/API” requirements for tools and hardware vendors @karpathy.
• Simon Willison — the best micro-case study of parallel-agent failure/recovery using session logs as the source of truth @simonw +1.
• Steve Ruiz (tldraw) — pragmatic company-building: code gets easier, but alignment/positioning/communication get harder—and he’s automating the overhead away Changelog.
• Theo — sharp practitioner critique on Claude Code regressions plus continued pressure on “harness vs infra” policy differences across vendors @theo +1.
• François Chollet — frames agentic coding as ML optimization (spec/tests as constraints) and asks what the “Keras of agentic coding” will be @fchollet; @swyx suggests DSPy as the presumptive community default @swyx.

🎬 WATCH & LISTEN

1) Boris Cherny — “Plan mode” as the default starter move (~1:09:52–1:10:41)

Hook: a simple, copyable workflow: force planning first (no code), iterate the plan, then execute + auto-accept when the plan is solid Lenny's Podcast.

2) Boris Cherny — “Coding is largely solved… what’s next?” (~0:18:19–0:19:06)

Hook: his thesis on why the frontier is shifting from IDE coding to adjacent operational tasks and general automation Lenny's Podcast.

3) Steve Ruiz — daily automated release notes from landed PRs (~0:20:35–0:21:02)

Hook: treat agents like scheduled staff: every day, Claude scans the last 24h PRs and drafts “release notes we’d publish if we shipped main today” Changelog.

📊 PROJECTS & REPOS

• NanoClaw — “Clawdbot” in ~500–700 LOC TypeScript using Apple container isolation for sandboxing/security; posted as Show HN @betterhn20 +1. Repo: https://github.com/gavrielc/nanoclaw@betterhn20 • HN: https://news.ycombinator.com/item?id=46850205@betterhn20.
• Nullclaw — “fastest, smallest OpenClaw clone”: 678 KB static binary, no runtime/VM/framework overhead @jedisct1. Repo: https://github.com/nullclaw/nullclaw@jedisct1.
• tldraw agent starter kit — Cursor-like agent panel next to a canvas; cloneable starter for agent+canvas UX: https://tldraw.dev/starter-kits/agentChangelog.

Editorial take: As agents make code cheap, the new edge is orchestration discipline: plan-first loops, sandboxing, session-log recoverability, and AI-native interfaces that don’t force your agent to “be the computer.” Lenny's Podcast +3

Top Stories

1) Google ships Gemini 3.1 Pro, pushing reasoning + cost efficiency

Why it matters: This is a broad distribution release (consumer, developer, enterprise) paired with third-party benchmarking that emphasizes a price/performance edge—an increasingly decisive axis as models converge.

Google announced Gemini 3.1 Pro as the “core intelligence” behind Gemini 3 Deep Think, now scaled for practical applications @Google +1. Google positions it as a new baseline for complex problem-solving, citing 77.1% on ARC-AGI-2 (novel logic patterns), described as more than double Gemini 3 Pro @Google.

Independent benchmarking from Artificial Analysis reports Gemini 3.1 Pro Preview as the top model on its Intelligence Index, with a notable advantage in price and token efficiency: <$50% evaluation cost versus Claude Opus 4.6 (max) and GPT-5.2 (xhigh) @ArtificialAnlys. Artificial Analysis lists pricing at $2/$12 per 1M input/output tokens for Gemini 3.1 Pro Preview, with total eval cost $892 (vs $2,304 for GPT-5.2 xhigh and $2,486 for Opus 4.6 max) @ArtificialAnlys.

They also report reduced hallucination behavior on AA-Omniscience: hallucination rate reduced from 88% to 50% (and +17 Omniscience Index points) @ArtificialAnlys.

2) Gemini 3.1 Pro lands across major dev surfaces (and some tooling frictions show up)

Why it matters: “Model quality” only translates to user impact when it’s reachable in the tools people already use—and reliability of those surfaces can quickly dominate perception.

Rollout/availability highlights include:

• Gemini app + NotebookLM (consumers) and Vertex AI / Gemini Enterprise (enterprise) @Google
• Developers via preview in Gemini API / Google AI Studio@Google +1
• GitHub Copilot public preview; GitHub reports early testing shows high tool precision and efficient edit-then-test loops @github
• Perplexity upgraded Gemini 3 Pro → Gemini 3.1 Pro for all Pro/Max users (consumer + enterprise) @AravSrinivas +1
• OpenRouter availability (preview) @scaling01 +1

At the same time, some early users report friction in Google’s coding toolchain: Gemini CLI not showing Gemini 3.1 Pro after installation, and Antigravity issues including failing requests and confusing model attribution (e.g., selecting Gemini 3.1 Pro (High) but being told it’s “powered by Claude 3.7 Sonnet”) @Yuchenj_UW +1.

3) OpenAI expands enterprise + national footprint: India partnership, FedRAMP authorization, and usage growth signals

Why it matters: The combination of (1) large-scale partnerships, (2) compliance milestones, and (3) steep usage growth points to continued acceleration in production adoption.

OpenAI announced an “OpenAI for India” initiative, partnering with Tata Group to build “sovereign AI infrastructure,” drive enterprise transformation with the Tata ecosystem, and partner with institutions to advance education @snsf.

Separately, OpenAI is now FedRAMP 20x Low authorized (per an announcement linking to the FedRAMP marketplace listing) @cryps1s.

On usage, OpenAI shared metrics cited in posts:

• ChatGPT message volume grew 8× YoY@scaling01
• API “reasoning token consumption per organization” increased 320× YoY@scaling01
• “More than 9,000 organizations” processed >10B tokens, and nearly 200 exceeded 1T tokens@scaling01

4) Mistral releases Voxtral Realtime (open) for low-latency transcription

Why it matters: Open licensing plus sub-second latency is a practical combo for real-time voice products, where deployment constraints and responsiveness matter as much as raw accuracy.

Mistral released Voxtral Realtime, stating it achieves state-of-the-art transcription at sub-500ms latency and is released under Apache 2@GuillaumeLample. They also shared a technical report, model weights, and a playground @GuillaumeLample.

5) Specialized inference hardware bets: “the chip is the model”

Why it matters: With growing concerns about inference scarcity, approaches that hard-specialize silicon to a given model aim to dramatically reshape latency/cost tradeoffs.

Awni Hannun highlighted Taalas running Llama 3 8B at 16k tokens/s per user, describing the key idea as: “each chip is specialized to a given model. The chip is the model.” @awnihannun

Research & Innovation

Why it matters: This cycle’s research emphasizes agent realism (memory across sessions, tool use), faster generation paradigms (diffusion LM latency), and methods to make long context usable.

Agent memory: benchmarks that test use, not recall

New research introduces MemoryArena, a benchmark evaluating memory across interdependent multi-session tasks where agents must learn from prior interactions and apply knowledge later @dair_ai. The authors argue existing long-context memory benchmarks (e.g., LoCoMo) are misleading: high recall doesn’t ensure correct multi-session actions, and models that saturate those benchmarks can perform poorly in “real agentic scenarios” @dair_ai. Paper: https://arxiv.org/abs/2602.16313@dair_ai.

Iterative reasoning with summaries: InftyThink+

Researchers from Zhejiang University and Ant Group presented InftyThink+, which trains models to think → summarize → continue in loops, optimized with trajectory-level RL @TheTuringPost +1. Reported gains include +21% accuracy on AIME24, 32.8% lower latency, and 18.2% faster RL training@TheTuringPost. Paper: https://arxiv.org/abs/2602.06960@TheTuringPost.

Faster diffusion LMs via post-training: CDLM

Together Research introduced Consistency Diffusion Language Models (CDLM), a post-training recipe for block-diffusion models targeting KV-cache incompatibility and high step counts @togethercompute. On Dream-7B, they report 4.1–7.7× fewer refinement steps and up to 14.5× lower latency with competitive math/coding accuracy @togethercompute.

Context compaction: Attention Matching (AM)

A new approach called Attention Matching (AM) proposes fast, high-quality context compaction in latent space, reporting 50× compaction in seconds with little performance loss vs summarization baselines @AdamZweiger.

Search/retrieval models: ColBERT-Zero

Researchers introduced ColBERT-Zero, a multi-vector model trained without distillation on top of dense models, claiming a new SOTA on BEIR using only public data @antoine_chaffin.

Safety in self-evolving agent societies: “self-evolution trilemma”

Researchers described a “self-evolution trilemma” for agent societies: you can’t simultaneously have continuous self-evolution, isolation, and stable safety alignment@TheTuringPost. They outline failure modes (consensus hallucinations, alignment drift, communication collapse) and mitigation ideas like external verifiers and checkpointing/rollback @TheTuringPost +1. Paper: https://arxiv.org/abs/2602.09877@TheTuringPost.

Products & Launches

Why it matters: The most durable gains come from shipping: models into workflows, tooling that reduces friction, and “agent ops” features that make systems observable and controllable.

Gemini 3.1 Pro: capability demos + access points

Google showcased Gemini 3.1 Pro building:

• A real-time ISS tracking dashboard combining public API telemetry, responsive UI, and physics-based day/night cycles @Google
• Website-ready animated SVGs generated from text prompts (pure code; crisp at any scale) @Google
• A 3D starling “murmuration” simulation reacting to hand-tracking with a generative score @Google
• A city planner app that tackles terrain, infrastructure mapping, and traffic simulation for visualization @GoogleDeepMind

Access points highlighted across announcements include Gemini App/NotebookLM for consumers and AI Studio/Gemini API for developers @sundarpichai.

ChatGPT: more interactive Code Blocks

OpenAI announced that Code Blocks in ChatGPT are “more interactive,” supporting writing/editing/previewing code in one place and previews for diagrams/mini apps (split-screen and full-screen views) @OpenAIDevs. They also called out previews for Mermaid flowcharts and debugging snippets @OpenAIDevs +1.

Claude in PowerPoint

Anthropic’s Claude in PowerPoint is now available on the Pro plan, and supports connectors to bring context from daily tools into slides @claudeai. Try it: https://claude.com/claude-in-powerpoint@claudeai.

W&B: Serverless SFT (public preview)

Weights & Biases launched Serverless SFT in public preview, with managed infrastructure powered by CoreWeave and features like training LoRAs and auto-deploying checkpoints; adapter training is free during preview@wandb +1.

Agent operations: tracing, filtering, and “agent trace search”

• Raindrop AI announced Trajectory Explorer, making agent decisions searchable “in seconds,” with emphasis on finding expensive or error-prone tool calls across traces @benhylak +1.
• LangSmith improved trace filtering UX (easier apply/edit; active filters visible at a glance) @LangChain.

Cursor: agent sandboxing on desktop OSes

Cursor rolled out agent sandboxing across macOS, Linux, and Windows; agents run freely inside a sandbox and request approval to step outside it @cursor_ai.

Industry Moves

Why it matters: Partnerships, capital, and distribution define which systems become defaults—especially for agents, where reliability and ops maturity are major differentiators.

Agent reliability and orchestration funding: Temporal

Temporal raised $300M Series D at a $5B valuation (led by a16z) to scale its open-source platform focused on making AI agents fault-tolerant by logging actions and enabling recovery from failures @dl_weekly.

Airtable announces Hyperagent

Airtable launched Hyperagent, positioning it as an agents platform where each session gets an isolated cloud compute environment (browser, code execution, image/video generation, data warehouse access, integrations, and skill learning for new APIs) @howietl. It also includes one-click Slack deployment and a “command center” to oversee fleets of agents @howietl.

Anthropic vs OpenAI revenue trajectory (Epoch AI)

Epoch AI Research reported that since each hit $1B annualized revenue, Anthropic has grown faster (10× vs OpenAI’s 3.4× per year) and “could overtake OpenAI by mid-2026” if trends continued @EpochAIResearch. Epoch notes extrapolations are aggressive and expects slowing; it also states Anthropic growth may have slowed to 7×/year since July 2025 @EpochAIResearch +1.

Model hosting + distribution signals

• Baseten announced GLM 5 live on its platform, positioning it around long-horizon agentic capabilities and tool calling for “real life” work use cases @basetenco.
• SambaNova promoted MiniMax M2.5 on SambaCloud for productivity agents, citing 80.2% SWE-Bench and 300+ t/s, with enterprise tier available now @SambaNovaAI.

Policy & Regulation

Why it matters: Compliance milestones unlock sensitive deployments; government actions can throttle or accelerate autonomy adoption.

OpenAI: FedRAMP authorization

OpenAI has achieved FedRAMP 20x Low authorization, with a link to the FedRAMP marketplace listing @cryps1s.

Autonomous vehicles: New York pauses robotaxi expansion

TechCrunch reported that New York hit the brakes on a robotaxi expansion plan@TechCrunch.

India: Google’s AI Impact Summit updates

At the AI Impact Summit in India, Google announced several accessibility and safety-related AI updates, including a live speech-to-speech translation model (real-time conversations in 70+ languages) and noting SynthID verification usage “over 20 million times” since November @Google.

Quick Takes

Why it matters: Smaller launches and sharp observations often foreshadow where the next wave of engineering effort is going.

• Tool calling risk: Researchers warned that some LLMs may request calling tools that were not provided in the allowed list—raising access-control concerns; one post claims this impacts major US providers except OpenAI @jeremyphoward.
• Embeddings: Jina released jina-embeddings-v5-text with small (677M) and nano (239M) variants, including a decoder-only + last-token pooling design and multiple LoRA adapters selectable at inference @JinaAI_ +1.
• Real-time speech: Voxtral Realtime resources include the arXiv report and HF weights @GuillaumeLample.
• ChatGPT growth: Technology sector seen as “over 10×” YoY growth (per one post) @scaling01.
• Benchmarking agents: Official SWE-bench leaderboard updated using the same scaffold (mini-SWE-agent v2) with cost analysis and trajectories @KLieret.
• Anthropic political spend: QuiverQuant reported Anthropic put $20M into a super PAC supporting candidates favoring more extensive AI regulation @QuiverQuant.
• Human detection limits: A study report said participants (including “super-recognisers”) performed barely better than chance at spotting AI-generated faces, despite high confidence @kimmonismus.
• Compute as a productivity constraint: Candidates are increasingly asked about dedicated inference compute for Codex, with usage per user growing faster than user count—suggesting scarcity @thsottiaux.
• Prompt caching: A guide describes prompt caching as a “most bang for buck” optimization for agent workflows, and Anthropic added automatic prompt caching to its API so devs don’t set cache points manually @dejavucoder +1.
• Gemini on ARC-AGI-3 harness: A reported config bug initially called Gemini 3.0 Pro instead of 3.1; after fixes, Gemini 3.1 Pro showed “much better performance” and could solve some games @scaling01.

Most compelling recommendation (highest operator leverage)

Paul Graham — “Founder Mode” (essay)

• Title: “Founder Mode” The Peel with Turner Novak
• Content type: Essay The Peel with Turner Novak
• Author/creator: Paul Graham The Peel with Turner Novak
• Link/URL: Not provided in the source excerpts
• Who recommended it: Garry Tan (Y Combinator President & CEO) The Peel with Turner Novak
• Key takeaway (as shared): Post–product-market fit, “leadership is…presence, not absence,” and the common advice to hire great people and “give them the keys to [the] kingdom” can backfire—creating “a company that I did not recognize.” The Peel with Turner Novak
• Why it matters: It’s a direct, experience-backed prompt for founders to stay actively involved as the company scales, especially after PMF when delegation patterns harden. The Peel with Turner Novak

Companion listen (same theme):

Social Radars — episode with Brian Chesky & Jessica Livingston on “Founder Mode” (podcast episode)

• Title: Social Radars episode on Founder Mode (Brian Chesky with Jessica Livingston) The Peel with Turner Novak
• Content type: Podcast episode The Peel with Turner Novak
• Author/creator: Social Radars (episode featuring Brian Chesky and Jessica Livingston) The Peel with Turner Novak
• Link/URL: Not provided in the source excerpts
• Who recommended it: Garry Tan The Peel with Turner Novak
• Key takeaway (as shared): Tan points to Chesky’s reflections (including remaking Airbnb during COVID) as a practical complement to the “Founder Mode” idea. The Peel with Turner Novak
• Why it matters: A founder-case narrative that Tan found instructive alongside the essay. The Peel with Turner Novak

AI & building: principles + mental models

Richard Sutton — “The Bitter Lesson” (blog post)

• Title: “The Bitter Lesson” Lenny's Podcast
• Content type: Blog post Lenny's Podcast
• Author/creator: Richard Sutton Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny (Head of Claude Code at Anthropic) Lenny's Podcast
• Key takeaway (as shared): “The more general model will always outperform the more specific model.” Lenny's Podcast
• Why it matters: A concise heuristic for AI product and research strategy—prioritizing general approaches over narrow, hand-crafted specialization. Lenny's Podcast

“Keep your identity small” (essay/blog post)

• Title: Essay/blog post advising “keep your identity small” No Priors: AI, Machine Learning…
• Content type: Blog post / essay No Priors: AI, Machine Learning…
• Author/creator: A founder from Applied Intuition (name not specified) No Priors: AI, Machine Learning…
• Link/URL: Not provided in the source excerpts
• Who recommended it: Speaker A (in a conversation featuring Elad Gil) No Priors: AI, Machine Learning…
• Key takeaway (as shared): “Keep your identity small,” described as “wonderful overall advice for this period of time.” No Priors: AI, Machine Learning…
• Why it matters: A direct reminder to stay adaptable—avoid over-anchoring to a fixed self-concept while circumstances shift. No Priors: AI, Machine Learning…

Deep work: one technical book pick

Functional Programming in Scala (book)

• Title: Functional Programming in ScalaLenny's Podcast
• Content type: Book Lenny's Podcast
• Author/creator: Not specified in the source excerpts
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Called “the single best technical book” he’s read; highlights “elegance” in functional programming and “thinking in types,” and frames it as something that can “level you up.” Lenny's Podcast
• Why it matters: Recommended as a durable way to improve how you think about code—not just a language-specific manual. Lenny's Podcast

Fiction that shaped leaders’ thinking (and taste)

J.R.R. Tolkien — The Lord of the Rings (book)

• Title: The Lord of the RingsThe Generalist
• Content type: Book The Generalist
• Author/creator: J.R.R. Tolkien The Generalist
• Link/URL (as shared): https://www.amazon.com/Lord-Rings-J-R-R-Tolkien/dp/0544003411The Generalist
• Who recommended it: Markus Villig (Founder & CEO, Bolt) The Generalist
• Key takeaway (as shared): Villig credits it with shaping his worldview. The Generalist
• Why it matters: A rare, explicit “this shaped how I see the world” endorsement from a founder. The Generalist

“Strauss” — Accelerando (book)

• Title: AccelerandoLenny's Podcast
• Content type: Book Lenny's Podcast
• Author/creator: “Strauss” (as stated in the excerpt) Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Recommended for capturing “the essence of this moment” through its accelerating pace. Lenny's Podcast
• Why it matters: A narrative frame for understanding rapid technological change—recommended specifically for its sense of speed and escalation. Lenny's Podcast

Cixin Liu — The Wandering Earth (short story collection)

• Title: The Wandering EarthLenny's Podcast
• Content type: Short story collection Lenny's Podcast
• Author/creator: Cixin Liu Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Highlighted for “amazing stories” and for offering a “different perspective than Western sci fi.” Lenny's Podcast
• Why it matters: A specific pointer to broaden creative inputs—both stylistically and culturally—via sci-fi with a distinct viewpoint. Lenny's Podcast

Business & crypto: two listens worth queuing

Acquired — start with the Nintendo episode (podcast)

• Title: Acquired (podcast) Lenny's Podcast
• Content type: Podcast Lenny's Podcast
• Author/creator: Ben and David (as stated in the excerpt) Lenny's Podcast
• Link/URL: Not provided in the source excerpts
• Who recommended it: Boris Cherny Lenny's Podcast
• Key takeaway (as shared): Praised for making “business history…alive,” with a specific suggestion to begin with a Nintendo episode. Lenny's Podcast
• Why it matters: A concrete entry point into a long-running show—recommended for clarity and storytelling around business history. Lenny's Podcast

@programmer at EthereumDenver — talk on the x402 protocol for agentic payments (video)

• Title: Talk on the x402 protocol for agentic payments @brian_armstrong
• Content type: Video talk @brian_armstrong
• Author/creator: @programmer @CoinbaseDev
• Link/URL (as shared): https://www.youtube.com/watch?v=MeTEQ4pHv3U@CoinbaseDev
• Who recommended it: Brian Armstrong (Coinbase CEO) @brian_armstrong
• Key takeaway (as shared): Armstrong calls it a “Good talk” on x402 for agentic payments. @brian_armstrong
• Why it matters: A direct founder endorsement of a concrete technical talk in the “agentic payments” space. @brian_armstrong

Big Ideas

1) Customer feedback has maturity phases—and teams need to upgrade their approach as they scale

Bir Khan (VP Product at Enterpret) shared a maturity framework that maps how teams evolve in customer feedback handling as org size and feedback volume grow Product School.

• Phase 1 (1–3 PMs): Intuition works; you can read most feedback (a few hundred items) and use simple tools like spreadsheets and quick AI summaries Product School.
• Phase 2 (5–6 PMs, ~2k–5k feedback/year): Bias creeps in (over-relying on power users), LLMs struggle with context, and manual tagging becomes a recurring tax; insights arrive in batches—often after decisions are already made Product School.
• Phase 3 (6+ PMs, >5k feedback/year): Manual workflows break; “volume-based” prioritization fails across segments (e.g., a few enterprise requests can matter more than many low-revenue requests); teams need a shared view of customer reality linked to revenue/retention/satisfaction metrics Product School.

Why it matters: If your tooling stays “Phase 1/2” while your org moves into Phase 3, you’ll feel it as slower planning cycles, more manual work, and leaders becoming disconnected from what customers are saying Product School.

How to apply: Use the phase descriptions as a diagnostic in your next planning retro: identify your current phase and explicitly decide what you’ll stop doing manually (e.g., tagging) vs. what must become continuously updated intelligence Product School.

2) Some “growth problems” are product problems in disguise

Andrew Chen’s heuristic from “20+ years working in startups”: most founders don’t have a growth problem—they have a product problem disguised as a growth problem@andrewchen.

Why it matters: It’s a reminder to pressure-test the underlying product value (and user reality) before defaulting to acquisition tactics.

How to apply: When growth stalls, run a quick product-reality check: what are the top recurring pain points, which are tied to high-value segments, and what’s the measurable impact of fixing them (see the tactical dashboards below) Product School.

3) In agentic AI, the “breakthrough” is often the interaction model—not raw capability

Sachin Rekhi frames a shift in AI tooling as a change in how tools interact with users:

• Claude Code: “controlled power” via confirmations, scoped access, and visible actions @sachinrekhi.
• OpenClaw: “frictionless autonomy,” with full system access and multi-step execution without approvals—bringing risks like unintended destructive actions, prompt injection with real consequences, silent data exposure, and zero audit trail @sachinrekhi.

He draws a parallel to Zoom: removing friction (no accounts, one-click links, simple setup) drove adoption @sachinrekhi, but also enabled Zoom-bombing—because some “friction” was actually security @sachinrekhi.

Why it matters: PMs designing AI agents will win or lose on where they place checkpoints (approvals, scope limits, audit logs)—not just on model performance @sachinrekhi.

How to apply: Treat “friction” as a design lever: remove it from onboarding and obvious flows, but add it back exactly at high-risk actions (data access, destructive commands, external communications) @sachinrekhi.

4) Roles are blurring: “everyone codes,” and generalists may be rewarded

In a conversation about Claude Code, Boris Cherny describes teams where PMs, designers, and others code, and argues many of the most effective people “cross over disciplines” Lenny's Podcast. He also predicts roles may get “murkier,” to the point where “software engineer” gets replaced by “builder,” with “everyone’s going to be a product manager and everyone codes” Lenny's Podcast.

Why it matters: If your org is adopting AI coding tools, the competitive advantage may shift toward hybrid builders who can connect product, design, business, and user context—not just write code Lenny's Podcast.

How to apply: Encourage (and reward) cross-functional “small loop” work: rapid prototyping, direct user contact, and shipping plus measurement—especially in early exploration Lenny's Podcast.

Tactical Playbook

1) Planning: prioritize feedback by business impact, not just volume

Enterpret’s guidance: traditional feedback analysis overweights counts (tickets/requests) and can be misleading; instead, quantify feedback with business context—revenue impact, NPS impact, and customer segment fit Product School.

Step-by-step:

1. Define your weighting dimensions: revenue impact, NPS/satisfaction impact, and segment/ICP fit Product School.
2. Connect data sources: integrate CRM/customer revenue data into your feedback system so requests can be ranked by revenue influence, not just number of mentions Product School.
3. Build a planning dashboard: rank feature requests and complaints by revenue/retention/satisfaction potential (vs. “loudest voice wins”) Product School.
4. Avoid “stale tagging” at scale: if manual tagging can’t keep up with tens of thousands of records, treat that as a system constraint (not a people problem) Product School.

2) Scoping: use AI to accelerate discovery—but don’t skip direct user research

Khan’s stance is explicit: “Nothing beats talking to users directly”—AI doesn’t replace it Product School. But AI can act as a force multiplier to validate hypotheses quickly and identify the right users to talk to Product School.

Step-by-step:

1. Write your hypothesis and ask: “What are the top pain points users mentioned about [flow]?” to get an answer grounded in your existing feedback corpus Product School.
2. Ask for targeting lists: “Who requested [integration] over the last six months?” so you can reach out for interviews Product School.
3. For alpha/beta recruiting: ask for customers who complained about the current capability—they’re strong early-access candidates Product School.
4. Before key customer meetings, ask for a short brief: top issues, past feature requests, and sentiment over time to reduce prep time Product School.

3) Post-launch: make “before/after” dashboards a habit (so you can prove impact)

Khan argues many teams “ship the feature, celebrate and move on,” without building the muscle to learn from launches Product School. The fix: launch-specific dashboards and before/after analysis to quantify outcomes Product School.

Step-by-step:

1. Define the pre-launch baseline (tickets, request volume, revenue impact, sentiment) Product School.
2. Ship.
3. Track the post-launch delta and package it for leadership.
4. Use a concrete narrative: “We were getting X complaints, shipped the fix, now we’re getting Y—here’s customer impact” Product School.

Example metric: a login bug fix reduced support tickets from 2,000/week to 200/week (90% reduction)Product School.

4) Stakeholder management: prevent “proposals” from silently turning into commitments

A recurring pitfall from a leadership-meeting pattern: a proposal floated under urgency shifts from “what if we just…” to “okay, so we’re doing this,” and soon it’s on a slide as a commitment r/prodmgmt. Even with guardrails like “This isn’t commitment” and “We need to validate first,” “9 times out of 10, the first proposal becomes the plan” r/prodmgmt.

Step-by-step (lightweight meeting guardrail):

1. Label agenda items explicitly as Explore / Decide / Commit (don’t rely on verbal caveats alone) r/prodmgmt.
2. Require a named validation step when someone says “we need to validate first” (owner + what “validated” means + when you’ll revisit) r/prodmgmt.
3. If you must set timing, separate “date for the date” (decision date) from delivery commitments r/prodmgmt.

5) Designing agent friction: relocate checkpoints instead of removing them

Rekhi’s punchline: “Friction isn’t the enemy. Badly placed friction is.” Winners won’t choose between speed and safety—they’ll redesign where friction lives so users get both @sachinrekhi.

Step-by-step:

1. List the agent’s high-risk actions (data access, deletions, sending messages, system changes) @sachinrekhi.
2. Decide which require confirmations and scoped access, and ensure visible actions and an audit trail in UX @sachinrekhi.
3. Remove friction elsewhere (setup, joining, onboarding), but treat security friction as intentional, not accidental @sachinrekhi.

Case Studies & Lessons

1) Claude Code: a “build for the model six months from now” bet + adoption metrics

Lenny shared that Claude Code launched one year ago and “today it writes 4% of all GitHub commits, and DAU 2x’d last month” @lennysan.

A core product bet (from Cherny):

Takeaways for PMs:

• If your product is gated by model capability, your roadmap may need to anticipate near-term model shifts @lennysan.
• Track adoption with concrete leading indicators (e.g., commit share, DAU acceleration) @lennysan.

2) Cowork: spotting latent demand from “misuse,” then building a safer, more accessible product

Cherny describes a “latent demand” pattern: users “abuse” a product to do things it wasn’t designed for—and that teaches you where to take it next Lenny's Podcast.

In Claude Code, they saw extensive non-coding use cases (e.g., growing tomato plants, genome analysis, recovering corrupted photos) and people “jumping through hoops to use a terminal” Lenny's Podcast. That became a signal to build something purpose-built Lenny's Podcast.

Implementation detail: Cowork was created by putting Claude Code into the desktop app; it included “a very sophisticated security system” with guardrails (including shipping a virtual machine), was built in ~10 days (entirely with Claude Code), and launched early while still “rough around the edges” to learn from feedback Lenny's Podcast.

Takeaways:

• Misuse is a discovery channel: watch for repeated “workarounds” that indicate demand Lenny's Podcast.
• Accessibility often requires safety/guardrails, not just a new surface area Lenny's Podcast.

3) ShowMe: multi-agent AI sales reps, staged rollout, and visibility as a trust mechanism

ShowMe’s origin: lots of website visitors weren’t converting; putting a human sales rep in front of visitors improved conversion, but it was too costly for unqualified leads—AI could filter and route accordingly Product Talk. They also found a free trial motion didn’t work due to a complex product and late “aha moment” Product Talk.

What they built: AI “digital workers” that behave like teammates—sales skills (voice/text/video calls, screen share, demos, phone calls, messaging) plus operational skills (reporting via Slack/email, sharing metrics) Product Talk.

How it evolved:

• MVP in ~2 weeks: voice agent narrating selected product videos with Q&A; used by a first external customer despite clunkiness Product Talk.
• Expanded to support different buyer stages (e.g., pricing, customer stories) and added a realistic avatar (HeyGen) and a Zoom-like UI because users were already trained on video-call affordances—and talked more to the AI “as a human” Product Talk.
• Split complex conversations into multiple agents (greetings/discovery, qualifying, pitching) due to model limitations and voice-latency constraints Product Talk.

Quality + rollout loop:

• Start with a POC, validate what the customer is trying to prove, and roll out via A/B tests (often starting with lower-quality leads) before full rollout over 1–2 months Product Talk.
• Build customer confidence via visibility: share conversations in Slack, log into CRM, and provide dashboards so customers can see interaction quality—not just be told it’s good Product Talk.
• Use customer reviews to generate tests and rerun conversations until passing, building a growing “battery” of tests to preserve improvements over time Product Talk.

4) Zoom: removing friction drove adoption—then security had to catch up

Zoom’s advantage versus Webex was removing friction (no accounts, one-click links, simpler setup, fast joins, effortless screen share), which drove adoption @sachinrekhi. But it also enabled Zoom-bombing, illustrating that some friction was actually security @sachinrekhi.

PM takeaway: Optimize friction placement, not friction elimination @sachinrekhi.

Career Corner

1) Become more of a generalist (and learn to cross boundaries)

Cherny’s advice: “try to be a generalist more than you have in the past,” noting that many effective engineers and PMs “cross over disciplines” (product + infra, product + design, business sense, user empathy) Lenny's Podcast. He describes a team dynamic where “everyone codes,” including PM, EM, designer, finance, and data science Lenny's Podcast.

How to apply: Pick one adjacent skill to deepen over a quarter (e.g., code enough to prototype, or do more user conversations) and use it to close a loop end-to-end on a small feature Lenny's Podcast.

2) Practical portfolio signal: owning outcomes across startup + enterprise contexts

A job seeker summary (ProductManagementJobs) highlights an arc many PMs will recognize:

• Founded a hyperlocal food delivery startup: managed a team of 20+, did 50+ customer interviews, built roadmap and GTM; learned from failure about product-market fit and unit economics r/ProductManagementJobs.
• Owned product frontend for an enterprise AI platform used by Fortune 500 supply chain teams, translating complex AI workflows into intuitive interfaces r/ProductManagementJobs.

How to apply: If you’re job searching, capture this as an “outcomes + learning” narrative: what you shipped, what changed, and what you learned from failure (explicitly called out as valuable) r/ProductManagementJobs.

Tools & Resources

1) An “AI PM tool stack” (one tool per category)

Aakash Gupta shared a tool stack used by top AI PMs, recommending you only need one tool per categoryAakash Gupta.

• Building
  • Vibe Coding: Cursor, Claude Code, Windsurf, Replit, Warp Aakash Gupta
  • Prototyping: Lovable, Bolt, v0, Magic Patterns, Base44 Aakash Gupta
• Productivity
  • Dictation: Wispr Flow, superwhisper, Tactiq, Speechify Aakash Gupta
  • Meetings: Granola, Fathom, Otter.ai, tl;dv, Fireflies Aakash Gupta
  • General LLMs: Claude, ChatGPT, Gemini, Kimi, Grok Aakash Gupta
• Automation
  • Simple Agents: Zapier, Lindy, Relay, Bardeen, Parabola Aakash Gupta
  • Full-Featured Agents: n8n, make, Activepieces, Workato, Tray Aakash Gupta
• Discovery
  • User Research: NotebookLM, Perplexity, Elicit, Consensus, Grain Aakash Gupta
  • Customer Intelligence: Dovetail, Unwrap, Enterpret, Monterey, Viable Aakash Gupta

2) Watch / read

• Product School (Enterpret VP Product): “Product Strategy Lessons from Notion, Stripe & Google” https://www.youtube.com/watch?v=AMUe4wBvNpwProduct School
• Lenny’s Podcast (ShowMe): “Building AI Sales Reps…” https://www.youtube.com/watch?v=5jMleOuL7SoProduct Talk
• Lenny’s Podcast (Boris Cherny / Claude Code): “Head of Claude Code…” https://www.youtube.com/watch?v=We7BZVKbCVwLenny's Podcast

3) Reading for stakeholder-meeting dynamics

• “How Chaos Gets Codified” (linked from the Reddit thread): https://notthoughtleadership.substack.com/p/how-chaos-gets-codified-when-proposalsr/prodmgmt

Market Movers

USDA 2026/27 outlook: fewer corn acres, more soybean acres

USDA’s 2026/27 outlook discussion across market sources converged on a corn acreage cut alongside a soybean acreage increase.

• Corn: projected around 94M planted acres with 183 bpa yield and about 1.837–1.84B carryout@MarketMinuteLLC +1
• Soybeans: projected around 85M planted acres, 53 bpa yield, and 355M carryout@MarketMinuteLLC +1
• Wheat: projected around 45M planted acres, 50.8 yield, and 933M carryout@MarketMinuteLLC +1

One summary of the release described corn acres “a little lower than expected” with carryout at 1.8, bean acres higher with carryout at 355, and wheat steady year-over-year at 933@GoddessofGrain.

Price action (Feb. 19): soybeans and wheat firmer; corn lagged

• Futures snapshot (Feb. 19): March corn $4.27¼; March soybeans $11.37¾; March Chicago wheat $5.53¾; March KC wheat $5.58¼; March spring wheat $5.77½Grain Markets and Other Stuff.

Market commentary tied wheat strength to short covering/technical buying, with additional support from production concerns in the U.S. and the Black Sea plus Middle East geopolitical risksFarm Journal.

Corn was described as struggling near chart resistance (430–435) into first notice day/delivery dynamics Farm Journal, while also being weighed down by a “too much of it” framing in one discussion of supply Farm Journal.

KC wheat: breakout + hedging signals

KC wheat was flagged at 8‑month highs and “trading to its highest levels since summer” @MarketMinuteLLC. Technical notes said KC wheat broke out of a multi‑year wedge and is on its longest sustained rally since 2021@MarketMinuteLLC, now pressing a key resistance level where a break “could spark further upside” @MarketMinuteLLC.

A producer-focused hedge alert noted KC wheat is nearing a target discussed “for months” Market Minute LLC and suggested, for those needing cash flow and unable to store, incremental sales or options protection (puts/calls)Market Minute LLC.

Innovation Spotlight

Brazil (Santa Catarina): open‑pollinated corn variety targets stability under drought

At the Itaipu Rural Show, Ipagri highlighted a new open‑pollinated corn variety (VPA) with lower seed cost, current yields around 160–165 sacas/ha, and potential up to 200 sacas/haCanal Rural. The variety’s longer flowering period was presented as a way to reduce losses during dry spellsCanal Rural.

Brazil: scaling ag-tech depends on shared connectivity models

ABDI described connectivity as a core bottleneck for “agriculture 4.0,” citing 70% of Brazil’s territory without connectivity coverage and highlighting shared infrastructure (e.g., cooperative antennas/satellite) as a practical model for smaller producers Canal Rural.

A Rio Grande do Sul example described a microclimate platform that aggregates multiple weather stations and offers access for about R$100/month, making data services cheaper for smallholders than buying their own stations Canal Rural.

Regenerative systems: new efforts to standardize definitions and measurement

A Spain-based effort set out 10 consensus-based scientific criteria for regenerative agriculture and cited findings that regenerative soils can sequester at least 35% more carbon and buffer summer ground temperatures by up to 3.6°C, while producing similar yields at similar or lower cost after transition This Week In Regenerative Agric….

Regional Developments

Brazil (Mato Grosso): harvest delays and quality loss from excess rain

In Paranatinga (MT), reports described persistent rain disrupting soybean harvest and raising uncertainty about losses Canal Rural. One producer cited 80 hectares ready for harvest but inaccessible due to moisture, with grain rotting and germinating; he estimated about 50% loss already and warned the field could be 100% lost if harvest couldn’t occur within 2–3 daysCanal Rural.

Conab progress updates said Brazil’s soybean harvest reached 24.7% of area but remained behind last year and the 5‑year averageCanal Rural.

Second‑crop corn planting was described as delayed by rain that slows soybean harvest (about 3% behind last year and nearly 6% behind the 5‑year average) Canal Rural. Mato Grosso was a bright spot at about 52.7% planted—9.4% ahead of last yearCanal Rural.

Brazil logistics: BR‑174 road conditions raise costs (Juína, MT)

Producers described BR‑174 (Juína–Vilhena corridor) as strategically important for flows toward Porto Velho Canal Rural, but reported severe road impacts—e.g., 11 km taking ~1 hour for a truck Canal Rural and tire damage costing R$6–7k on a short trip Canal Rural. Local basis impacts were also noted: soybean prices cited around R$92–93/sack in Juína vs R$100–103 in Campo Novo, with losses of about R$10/sack attributed to logistics Canal Rural.

Argentina: short strike halts grain shipments

A 48‑hour strike by Argentine maritime workers was reported as bringing grain shipments “to a standstill,” with the main oilseed crushers union planning a separate 24‑hour strike tied to proposed labor reforms Grain Markets and Other Stuff.

Black Sea: Ukraine winter wheat risk in the news flow

KC wheat strength was linked by some market commentary to Ukraine winter wheat risks following a thaw and then cold snap, according to Ukraine’s farmers union Grain Markets and Other Stuff.

Best Practices

Grain marketing: align hedges with storage and cash-flow constraints

• For producers unable to store wheat and needing cash flow, one hedge alert favored incremental sales or option structures (puts/calls or combinations) Market Minute LLC.
• For old-crop corn needing movement before spring, a separate recommendation emphasized downside protection, noting corn options were “so cheap it makes no sense” to be unprotected if movement is required soon Market Minute LLC.

Fertility placement: when banding can (and can’t) reduce fertilizer rates

Ag PhD highlighted interest in banding vs. broadcasting to reduce rates under high fertilizer prices, noting improved extraction and reduced tie-up when fertilizer is placed where roots will find it—especially for immobile nutrients like P, K, zinc, and copper@AgPhDMedia. It cautioned that banding offers less benefit for leachable nutrients (nitrate, sulfate, boron) and warned about salt injury risk near seed (lower rates and more distance are safer) @AgPhDMedia.

Livestock (Brazil): rainy-season supplementation targets measurable gain

A Brazil segment described how balancing energy, protein, and minerals during the rainy season can raise daily gains from about 450g up to 900g per animal/day, improving cash flow and shortening production cycles Canal Rural. A worked example described a 9‑arroba heifer reaching 12 arrobas in 90 days with energy supplementation (vs. about 6 months otherwise) Canal Rural.

Input Markets

Fertilizer remains a focal variable for 2026 row-crop decisions

One market discussion repeatedly emphasized fertilizer cash prices as a key variable in corn/soy acreage decisions, with observations of fertilizer movement “on fire” and farmers filling sheds later than usual (suggesting deferred fall buying) Farm Journal.

Agro policy also moved into the input narrative: President Donald Trump designated glyphosate-based herbicides and elemental phosphorus as critical to national defense, directing USDA to prioritize and secure domestic supplies @SuccessfulFarm.

Policy, demand, and crush: soybean oil for biofuel remains central

USDA’s outlook discussion included a projected record 17.3B lbs of soybean oil use for biofuel Farm Journal. Separately, USDA-ag-economy commentary framed the RFS/E15 debate and the 45Z rule (including flexible feedstock provisions) as “wildcards” that could affect corn/soy demand—and potentially the acreage and price outlook—depending on timing Farm Journal.

Forward Outlook

Watch-list: policy timing + trade headlines as near-term volatility sources

• USDA’s biofuel-policy framing explicitly highlighted the importance of the RFS/E15 pathway and the 45Z rule’s incentive structure as potential demand drivers for corn/soy/canola Farm Journal.

• Another market view tied recent soybean strength to improving U.S.–China relations and suggested markets may react to headlines in the lead-up to an April meeting; the same discussion recommended being “3/4 cash sold” on old-crop corn/soy/wheat ahead of that meeting window due to uncertainty Farm Journal.

Weather risk lens: KC winter wheat belt flagged for dry-spring and frost concerns

A weather-focused segment described a lag where sea-surface temperatures shifted toward neutral but the atmosphere remained “La Niña-like,” a pattern it said “always means a dry spring” for the KC winter wheat belt (KS/OK/NE), adding frost risk as wheat comes out of dormancy Farm Journal.