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Recent briefs
Deedy
Windsurf
Armin Ronacher
🔥 TOP SIGNAL
Warp’s Zach Lloyd is shipping the next layer of agent infrastructure: OZ, a cloud orchestration platform meant to move “too many agents on a laptop” into a managed, team-visible system (search, filters, PR tracking, conversation history) . The practical shift: once you’re running fleets of agents, visibility + coordination becomes the bottleneck at least as much as model quality .
🛠️ TOOLS & MODELS
Warp — OZ (new launch): Cloud agent orchestration platform positioned as “Vercel for agents,” aimed at teams outgrowing laptop CPU/RAM and needing centralized tracking/visibility .
- Usage signal: Warp users are launching ~3M agents daily.
- Business pace signal: first $1M ARR took ~1 year, now adding $1M every 5–6 days.
Zach Lloyd’s coding model pick (hands-on)
- Preferred model “as of last week”: Codex 5.3; previously Opus 4.6.
- Why: Codex 5.3 does better on “very complex hard problems” (at the cost of speed); he reports more context loss and partially-thought-through solutions with Opus 4.6 .
- Warp is model-routed: they plan to auto-pick models based on a pass over task complexity (no manual “rotation” by engineers) .
Codex CLI (Windows) — agent sandbox
- Codex announced “the world’s first coding agent sandbox for Windows,” enabling agents to run without approving specific commands.
- Requires Codex CLI v0.100.0+.
- Roadmap mentioned: IDE extension + Windows Codex app .
- Shared by Greg Brockman linking to the announcement .
Codex app — feature drop + Windows invites
- New: 5.3-codex-spark, forking, pop-out window, mark unread, perf/quality improvements (+ “a secret fun thing”) .
- “Tomorrow: first Windows alpha invites” .
- Pop-out window called out as useful for iterating alongside the browser / not losing context across workspaces .
Cursor — long-running agents (practitioner mention): Hosts report Cursor “long running agents” that can run “for hours” just released .
OpenClaw (beta) — v2026.2.13
- “Chunky” beta update shipped .
- Dev-reported improvements: tests ~2x faster and faster CLI load.
- PR counter: ~2700 → 2722.
CodexBar — v0.18.0-beta.3: New release for tracking token usage.
Windsurf — Arena Mode (model choice in the loop)
- Format: one prompt, two model outputs, you vote; positioned as a “real-world coding” benchmark vs traditional benchmarks .
- Free “for the next week” (post-launch) .
- @swyx’s reported outcome: “total anthropic victory” .
Benchmarks vs tool reliability (Gemini discourse)
- Reported: Gemini 3 Deep Think scored 3455 on Codeforces (claimed equivalence: “#8 best competitive programmer”), vs prior best 2727 for OpenAI o3 (claimed “#175”) .
- Theo’s usability critique: Gemini 3 Pro is “as smart as Opus 4.6” but “screws up tool calls” consistently .
Cline CLI 2.0 — Go → TypeScript rewrite (shipping reality)
- Team rewrote a bug-heavy CLI from Go to TypeScript to reuse existing TS code; result: “sleeker and usable,” enabling seamless agent evals in the CLI .
- Theo’s take: “go is a terrible language for CLIs” .
💡 WORKFLOWS & TRICKS
Run agents like a team, not a tab (OZ pattern)
- Problem: lots of agents on laptops → CPU/RAM/file space constraints, plus no centralized view of what’s happening .
- OZ’s approach: a web app showing all agents across a team; search/filter by PRs; record of conversations .
Expect “attention saturation” as your first scaling limit
- When you can’t context-switch fast enough, “we are the limiter of our total pipeline of work” → idea: an orchestrator agent overseeing other agents .
Terminal-as-control-plane for agents (vs IDE)
- Pitch: prompting in English replaces many command invocations, but you still want a record of what ran, plus multiple panes/sessions—making a terminal-like interface a better form factor than a “word processor interface” IDE .
- Warp’s “middle ground”: markdown viewer, file tree, native diff viewer → described as an “agentic development environment” .
Parallelize safely: split by non-overlap (and keep context clean)
- Armin Ronacher’s rule: don’t parallelize conflicting work; use sub-agents for research tasks that don’t change files; separate work by folder / backend vs frontend / git worktrees to avoid merge conflicts and reduce review overhead .
Structure-first, then delegate (repeatable “handoff” pattern)
- Start a new project by writing core structure/architecture by hand, then gradually give the agent more responsibility .
- Keep the most critical slice handwritten: “5–10% … really important crucial bit” gets more manual attention .
Write “dumber” code (fewer abstractions) to make agent + human debugging faster
- Argument: lots of abstraction layers increase time-to-fix during outages; LLMs are not good at abstractions but are good at straightforward solutions .
- Concrete example: choose raw SQL over an ORM—agents can write SQL, making the trade-off less painful .
Sandboxing and “YOLO flags” are converging on the same question
-
Simon Willison asks whether people run
--yolo(Codex) /--dangerously-skip-permissions(Claude Code), and if they YOLO in a sandbox . - Codex’s Windows sandbox pitch: let the agent work without per-command approvals (within a sandboxed environment) .
-
Simon Willison asks whether people run
Operational safety: avoid insecure third-party OpenClaw setup services
- Warning from a tester: multiple setup services allegedly exposed the gateway, lacked pairing mode, and allowed internet discovery of the root directory—“DO NOT use” .
- Maintainer agrees: quick installers may skip encryption and not surface security docs .
Two “update/install via curl” patterns (use with eyes open)
-
OpenClaw beta update instructions: ask your agent to update to beta or run
curl -fsSL https://openclaw.ai/install.sh | bash -s – –beta. -
TinyClaw (alternative approach): install with a remote shell script then
tinyclaw start.
-
OpenClaw beta update instructions: ask your agent to update to beta or run
👤 PEOPLE TO WATCH
Zach Lloyd (Warp) — clear-eyed about where agents work today vs where they don’t (and why teams still hire senior engineers).
“Coding is not close to being solved… for hard, complicated software.”
Armin Ronacher (@mitsuhiko) — strong practitioner heuristics (tool loop, low abstraction code, parallelization) + sharp product ops.
- Disabled GitHub Copilot in his enterprise org until it improves .
Peter Steinberger (@steipete) — shipping fast on OpenClaw + calling out security footguns.
- Also ships tooling around agent usage: CodexBar for token tracking .
Alexander Embiricos (@embirico) — frequent Codex shipping notes; Windows sandbox + Codex CLI requirements are the kind of detail that matters in real workflows .
@arafatkatze / Theo — showing the unglamorous work (rewrites, UX bugs, eval plumbing) needed to make agent tooling actually usable .
🎬 WATCH & LISTEN
1) Warp launches OZ: “Vercel for agents” + team visibility (≈1:01:39–1:04:03)
Hook: A concrete picture of what breaks when everyone runs agents locally—and the primitives (search, PR filtering, conversation records) you need once agents become a shared team resource .
2) Why agents don’t replace engineers (yet): supervision + hard codebases (≈1:10:42–1:14:11)
Hook: Lloyd explains why Warp still hires engineers: agents need supervision, and “hard” product code (custom UI frameworks, bug-prone changes) punishes unsupervised automation .
3) “Dumb code” wins: fewer abstractions, faster recovery (≈0:11:55–0:15:22)
Hook: Ronacher’s argument for writing low-abstraction code (and even using raw SQL) because both humans and LLMs debug it faster when production breaks .
📊 PROJECTS & REPOS
- OpenClaw v2026.2.13 (beta release) — https://github.com/openclaw/openclaw/releases/tag/v2026.2.13
- OpenClaw beta install script — https://openclaw.ai/install.sh
- TinyClaw (OpenClaw-like in ~400 LoC) — https://github.com/jlia0/tinyclaw
- TinyClaw remote installer — https://raw.githubusercontent.com/jlia0/tinyclaw/main/scripts/remote-install.sh
- CodexBar v0.18.0-beta.3 — https://github.com/steipete/CodexBar/releases/tag/v0.18.0-beta.3
- Cline CLI 2.0 link — https://x.com/cline/status/2022341254965772367
- Windsurf Arena Mode post — https://x.com/windsurf/status/2017334552075890903
— Editorial take: We’re exiting the “which model is best?” phase and entering the ops phase: sandboxes, orchestration, visibility, and human attention are becoming the real limiting factors.
Anthropic
Dario Amodei
Ben Thompson
Frontier science: OpenAI showcases physics progress + a new “frontier proof” benchmark
GPT-5.2 contributes a new theoretical physics result (gluon amplitudes)
OpenAI says GPT-5.2 helped derive a new result in theoretical physics: a “single-minus” gluon interaction at tree level long treated as having zero amplitude can be non-zero in a carefully defined particle-alignment scenario . The work is being released as a preprint with collaborators from IAS, Vanderbilt, Cambridge, and Harvard, and OpenAI is soliciting feedback as it submits for publication .
Why it matters: This is a concrete claim of AI-assisted progress on a long-assumed “empty” case in amplitude theory, plus an explicit methodology report (simplification → conjecture → independent proof → author verification) .
Links: OpenAI post https://openai.com/index/new-result-theoretical-physics/ • arXiv https://arxiv.org/abs/2602.12176
Physicists’ reaction: “might not have been solvable by humans”
Greg Brockman relayed a conversation with physicists Andrew Strominger and Alex Lupsasca, describing an internal-model run that “solved AND proved a previously unsolved problem in quantum field theory…in 12 hours” . Strominger is quoted as saying:
“It is the first time I’ve seen AI solve a problem in my kind of theoretical physics that might not have been solvable by humans.”
Brockman also attributes the step-change to both model improvements and learning “how to talk to it,” adding Strominger’s view that many physicists may need to learn to interact with these systems to keep up with the frontier .
Why it matters: Beyond the preprint, this reflects a qualitative shift in how domain experts describe AI’s ceiling in their own research practice—especially for problems that are traditionally hard to verify quickly .
OpenAI launches “First Proof” to benchmark novel math research
OpenAI is now benchmarking models on novel frontier research via the “First Proof” challenge (http://firstproof.org) . In a week-long “side-sprint” with limited human supervision, an internal training model produced solutions judged “likely correct” for at least 6 of 10 unpublished problems (2, 4, 5, 6, 9, 10), though OpenAI notes the problems are difficult to verify and relied on expert feedback .
Why it matters: It’s an explicit move to evaluate models on new research targets (not just benchmark corpora), while also foregrounding the verification bottleneck as part of the measurement problem .
Open-source + product releases: agents, biology, and world-building
MiniMax open-sources “MiniMax-M2.5,” trained via RL in complex environments
MiniMax announced it has open-sourced MiniMax-M2.5, trained with reinforcement learning across “hundreds of thousands of complex real-world environments,” and claims state-of-the-art performance in coding, agentic tool use, search, and office workflows .
Why it matters: This is another step in the “closed vs. open” convergence narrative—amplified by Emad Mostaque’s claim that MiniMax is “closing the closed-open source frontier AI gap” .
Links: https://huggingface.co/MiniMaxAI/MiniMax-M2.5 • https://github.com/MiniMax-AI/MiniMax-M2.5
ByteDance releases Protenix-v1 for biomolecular structure prediction
A post shared on r/LocalLLM says ByteDance released Protenix-v1, an open-source model for biomolecular structure prediction described as achieving “AF3-level performance” .
Why it matters: Open releases in bio/structure prediction keep accelerating—expanding the set of serious, locally-runnable tools in a domain where model access and reproducibility matter.
Repo: https://github.com/bytedance/Protenix
Google DeepMind opens Project Genie world-building to U.S. AI Ultra subscribers
Google DeepMind announced Project Genie, framing it as a system that “built” explorable worlds from what users “dreamed” . It’s available for creation to U.S. Google AI Ultra subscribers via labs.google (Project Genie) .
Why it matters: World-generation is moving from demos to consumer-accessible creation flows—packaged as an interactive “world” product, not just a video output .
Link: https://labs.google/projectgenie
World Labs expands advanced 3D-world editing controls in “Marble”
World Labs says advanced editing is now available for all users, and Fei-Fei Li highlighted the new “Advance model” as providing more editable control when creating “a true 3D world” (e.g., changing room vibe, adding scenes outside windows, generating variations) .
Why it matters: The workflow emphasis is shifting toward iterative, controllable world-building (image → pano → edit → create world), not just one-shot generation .
Scaling economics + governance: Amodei on compute risk, diffusion, and regulation
Dario Amodei: demand prediction makes datacenter scaling financially perilous
In a recent interview, Anthropic CEO Dario Amodei argued that buying datacenters is risky because if you’re “off by a couple years,” it can be “ruinous” . He describes uncertainty around how quickly technical progress turns into revenue, and lays out why over-buying compute based on aggressive growth assumptions could drive bankruptcy if growth slows meaningfully .
Why it matters: It’s a clear articulation of why even teams with aggressive “powerful AI” timelines may still scale compute conservatively: diffusion and demand forecasting are constraints, not just engineering ambition .
Amodei calls for federal standards (and rejects a 10-year state moratorium)
Amodei criticized the idea of banning state AI regulation for 10 years without a concrete federal plan, calling 10 years “an eternity” given risks like bioterrorism and autonomy . He said he could support federal preemption in the form “here’s our standard; this applies to everyone,” and emphasized near-term urgency around transparency standards and targeted regulation if risks become clearer .
Why it matters: This is a specific regulatory posture from a leading lab: allow state action absent federal movement, but prefer a coherent federal baseline once feasible .
Anthropic expands Claude access via CodePath
Anthropic announced a partnership with CodePath to bring Claude and Claude Code to 20,000+ college students (community colleges, state schools, HBCUs) .
Why it matters: Distribution deals aimed at early-career developers are becoming a strategic channel—especially as coding agents become a core wedge product.
Link: https://www.anthropic.com/news/anthropic-codepath-partnership
Safety, trust, and geopolitics: xAI turmoil, “Adult Mode,” and hallucinations
Reported xAI restructuring includes safety-team concerns
Gary Marcus reposted reporting that former xAI employees described restructuring tensions over safety and being “stuck in the catch-up phase” . Marcus also quoted a claim that “Safety is a dead org at xAI” and noted that Musk’s shared org chart contained no mention of a safety team .
Why it matters: This is a notable governance signal: safety org structure (and perceived deprioritization) is becoming a public, competitive, and reputational factor .
Grok’s growth is tied to engagement dynamics (including sexual content)
A Big Technology report says Grok’s U.S. daily chatbot-app market share rose from 1.6% → 15.2% (Jan 2025 to Jan 2026), trailing only ChatGPT and Gemini . It also reports Grok’s user base is 82% male and highlights sexually oriented companion features and training processes involving reviewing sexual conversations (as reported by The Washington Post) .
Why it matters: It frames “companion” and adult content as an engagement lever in the chatbot app race—alongside safety and brand risk (including a reported spike tied to safeguards lapses) .
OpenAI’s reported plans for “Adult Mode” add pressure to the engagement race
The same report says OpenAI is planning an “Adult Mode” in ChatGPT in the coming months .
Why it matters: If accurate, it underscores how growth pressures can expand product scope into more controversial interaction modes—raising new policy, safety, and trust questions .
Marcus argues hallucinations remain a major real-world constraint
Marcus argued that hallucinations are still prevalent and subtle, citing: a rise in lawyer incidents involving fake cases (from ~100 to 900+ in under a year), an AIMultiple benchmark showing 15%+ hallucination rates across models, and a reported pharma study finding 26%–69% hallucination rates on challenging problems . He also cited a “Remote Labor Index” result that AI completed only 2.5% of sampled online human tasks .
Why it matters: Even amid capability breakthroughs, reliability remains a binding constraint for adoption—especially in high-stakes domains (law, pharma) .
Pentagon briefly adds (then removes) Alibaba/Baidu from a military-aid list
A r/LocalLLM post says the U.S. Pentagon added Alibaba, BYD, Baidu, and TP-Link to a list of companies “aiding the Chinese military,” then removed them minutes later without explanation—triggering stock drops and investor concern . Alibaba and Baidu denied military ties and said they focus on civilian AI applications .
Why it matters: Even transient designation events can move markets and intensify U.S.–China tech friction—relevant for AI supply chains, partnerships, and capital access .
Competing narratives: ads, AGI pace, and Hollywood’s response
Ben Thompson criticizes Anthropic’s Super Bowl messaging about “ads in answers”
In a Sharp Tech episode, Ben Thompson called Anthropic’s Super Bowl ad “despicable” and “lying,” arguing it depicted OpenAI inserting ads into responses even though OpenAI is not doing that . He characterized the move as “strategy credit,” arguing Anthropic is enterprise-focused and attacks an ad business it likely won’t build .
Why it matters: The “ads in assistants” debate is now also a brand war—and one that may influence talent, not just users .
Chollet: AGI won’t necessarily cause a sudden capability explosion
François Chollet argued that AGI’s rise won’t lead to a “sudden exponential explosion” in capabilities because of bottlenecks in sources of improvement, and that scaling intelligence in silicon doesn’t remove those bottlenecks . He framed AGI as part of a longer, essentially linear arc of scientific progress over centuries, not a single discontinuity .
Why it matters: It’s a prominent counterpoint to “takeoff” framing—useful context as more labs publicize frontier-science results and aggressive timelines .
Andrew Ng: Hollywood is anxious—but there’s common ground
After speaking at Sundance, Andrew Ng described Hollywood’s discomfort with AI companies learning from creative works without consent/compensation, and unions’ concerns about job displacement (e.g., SAG-AFTRA) . He argued there’s common ground around guardrails against deepfakes and upskilling, and said AI video tools could make creation easier for millions if developers and Hollywood collaborate .
Why it matters: Entertainment remains a high-signal arena for AI’s IP, labor, and deepfake debates—where “alignment” questions quickly become contract and governance questions .
Bloomberg
Andrej Karpathy
Kling AI
Top Stories
1) GPT‑5.2 helped derive (and prove) a new theoretical-physics result on gluon amplitudes
Why it matters: This is a concrete example of an LLM contributing to frontier research: simplifying previously intractable expressions, proposing a general formula, and supporting a proof workflow that the authors then verified—while also challenging a long-standing “zero amplitude” assumption in quantum field theory.
- What the preprint claims: A gluon interaction (“single-minus” at tree level) long treated as having zero amplitude can be non-zero under a carefully defined alignment condition.
- How it was found: The authors had computed results up to n=6 gluons by hand; GPT‑5.2 simplified the expressions and conjectured a general formula; a separate scaffolded internal model independently derived the same formula and produced a formal proof in about 12 hours; the authors verified the result.
- Implications noted by OpenAI: Finding structure in a case long thought empty “sharpens our understanding” and “opens new directions,” including extensions to gravity and related amplitude relations.
“It is the first time I’ve seen AI solve a problem in my kind of theoretical physics that might not have been solvable by humans.”
References: OpenAI write-up https://openai.com/index/new-result-theoretical-physics/ and arXiv https://arxiv.org/abs/2602.12176.
2) “First Proof” raises the bar for evaluating research-level math capability
Why it matters: Several discussions argue that novel frontier research problems are a more meaningful capability test than standard benchmarks—especially when problems are expert-domain and solutions are hard to verify quickly.
- The First Proof benchmark consists of 10 math research problems that research mathematicians have solved but not published; teams had a week to attempt solutions with LLMs.
- An internal model run with limited human supervision produced solutions that (based on expert feedback) have a high chance of being correct for at least 6/10 problems (2, 4, 5, 6, 9, 10).
- Method notes: one-week side sprint; no proof ideas given; some solutions expanded per expert feedback; manual back-and-forth with ChatGPT for verification/formatting; best attempts selected.
Site: http://firstproof.org.
3) Microsoft signals intent to build its own frontier foundation models
Why it matters: If Microsoft shifts more of its stack toward in-house foundation models, it could reshape the competitive dynamics of model access, distribution, and “default” enterprise AI infrastructure.
- In an FT interview (per reporting), Microsoft AI chief Mustafa Suleyman said: “We have to develop our own foundation models, which are at the absolute frontier, with gigawatt-scale compute and some of the very best AI training teams in the world.”
4) SWE-rebench: token budget and trace efficiency are now first-class constraints
Why it matters: For coding agents, capability is increasingly entangled with how effectively an agent spends its token budget across long tool-using trajectories—not just single-shot model quality.
- SWE-rebench’s January update highlights a ~1M token wall: beyond ~1M tokens per problem, additional tokens yield only marginal pass@1 improvements.
- The benchmark evaluates real-world SWE tasks in an iterative agent loop (read files → patch → run tests → refine), where token counts reflect the full trajectory.
- A “top cluster” (Claude Code, Claude Opus 4.6, gpt‑5.2‑xhigh) operates in the ~1–2M tokens/problem regime.
- Efficiency note: gpt‑5.2‑codex is called out as a notable exception—performing strongly below ~1M tokens/problem.
Separately, SWE-rebench reported Jan–Feb numbers with 46 new PRs, with Opus 4.6 and Codex 5.3 leading at 51.7% resolve rate.
5) MiniMax M2.5 continues to spread through agent tooling—alongside mixed reliability signals
Why it matters: A cheap, fast open(-ish) model can change what’s economically feasible for long-horizon agents, but real-world adoption will depend on reliability tradeoffs (e.g., hallucination and instruction following).
- MiniMax positions M2.5 as an open-source model trained via RL across hundreds of thousands of complex real-world environments, targeting coding/tool use/search/office workflows.
- Artificial Analysis describes M2.5 as an incremental upgrade over M2.1 (+2 Intelligence Index points to 42) with improved agentic performance (GDPval-AA ELO 1215 from 1079), but also a higher hallucination rate (reported at 88%, with AA-Omniscience Index dropping to -41 from -30).
Research & Innovation
Why it matters: This cycle’s technical work clusters around (1) better RL/post-training recipes, (2) architectures and systems to make long-horizon/long-context cheaper, and (3) data curation methods that scale beyond “one score.”
RL + post-training recipes and theory
- MaxRL (CMU/Tsinghua/Zhejiang/UC Berkeley): A sampling-based framework meant to bridge standard RL and exact maximum likelihood; reported to Pareto-dominate existing methods with up to 20× better test-time scaling efficiency than GRPO. Paper: https://arxiv.org/abs/2602.02710
- Length-Incentivized Exploration (LIE): RL recipe for test-time reasoning that uses a length reward plus a redundancy penalty to address the “Shallow Exploration Trap,” improving in-domain math and out-of-domain tasks; includes AIME25 gains (20.5% → 26.7%). Paper: https://arxiv.org/abs/2602.11748
- DPPO (Divergence PPO): Replaces token-ratio clipping with a full-distribution divergence constraint (TV/KL) to control harmful updates; claims include higher final rewards and stability without extra tricks. Paper: https://arxiv.org/abs/2602.04879
- RLER (RL with Evolving Rubrics) / Rubrics-as-Rewards: Maintains a buffer of per-prompt rubrics (seeded with search-grounded rubrics) and evolves them to stay discriminative as the policy shifts, including adding negative rubrics to target reward hacking. Paper: https://arxiv.org/abs/2511.19399
Efficient architectures and long-context behavior
- Transformer–SSM hybrid with 2% attention heads: Reports that scattering only “a single layer’s worth” of attention heads across the network can recover 95%+ of full Transformer performance on recall, math, and more; introduces Retrieval-Aware Distillation to keep only retrieval-performing heads, compressing hybrid state size by 5×. Paper: https://arxiv.org/abs/2602.11374
Data curation for multimodal and training mixes
- SkillRater (Perceptron.inc): Argues multimodal data quality is multidimensional; decomposes filtering into capability-aligned raters with near-orthogonal signals, reporting consistent improvements over monolithic scoring. Paper: https://arxiv.org/abs/2602.11615
- Olmix (Allen AI): Frames data mixing (web/code/math ratios) as a first-order lever on model quality and introduces a framework to configure and update mixing methods as datasets change during development.
Training/serving and model-building automation
- Custom CUDA kernels via an agent: Reports an agent that writes kernels tailored to a specific model + hardware instruction set, using benchmarks as rewards; kernels worked with Diffusers and
torch.compileand delivered speedups without quality loss (per the post). - vLLM on GB300: Reports DeepSeek R1 on GB300 achieving 22.5K prefill TGS and 3K decode TGS per GPU, described as an 8× prefill and 10–20× mixed-context improvement over Hopper; includes a recipe using NVFP4 + FlashInfer FP4 MoE kernel.
Products & Launches
Why it matters: Model improvements are increasingly “real” only when paired with distribution, integrations, and agent runtimes that users can adopt quickly.
Models and access
- GPT‑5.2 rollout + public testing: GPT‑5.2 is rolling out to everyone and is available in the Arena Text and Vision leaderboard for battle-mode testing; Arena notes the API name “gpt-5.2-chat-latest.”
- MiniMax‑M2.5 open-source distribution: Available on Hugging Face and GitHub, with day-0 ecosystem support (vLLM, and deployment partners).
Coding agents and dev workflows
- Cline CLI 2.0: Open-source coding agent running fully in the terminal, adding parallel agents, headless CI/CD support, and ACP support for multiple editors.
- Windows coding-agent sandbox: A “world’s first coding agent sandbox for Windows” claims safer agent operation without approving each command; live in the CLI with IDE extension and Windows Codex app planned.
- WebMCP starter template: Demonstrates agents interacting with websites without “seeing” the UI (browser-as-API), including a DoorDash-like checkout flow. Repo: https://github.com/Doriandarko/webmcp-starter
Multimodal creation and video
- Kling 3.0 in Video Arena: Positioned as an all-in-one multimodal creation engine; now testable in Arena’s video battle mode.
- Seedance 2.0 + agents: Chatcut reports Seedance 2.0 working with the OpenClaw agent to generate a UGC product video from an Amazon link (crawl page → extract info/photos → feed assets to Seedance).
- Google DeepMind Project Genie: Released for U.S. Google AI Ultra subscribers via https://labs.google/projectgenie
Platform tooling and observability
- Gemini Interactions API multimodal function calling: Tools can return actual images (not text descriptions), and Gemini 3 can process them natively; results can mix text + image.
- PostHog LLM Analytics + LlamaIndex integration: Tracks OpenAI usage (tokens, cost, latency) for a demo agent workflow.
- Stirrup agent harness speed tracking: Adds end-to-end speed metrics, per-model breakdowns for multi-model workflows, and tool-call duration tracking.
Industry Moves
Why it matters: Partnerships, funding, and platform shifts are shaping where models get adopted (education, enterprise, and developer ecosystems), not just who has the best benchmark score.
- Anthropic ↔ CodePath partnership: Anthropic is bringing Claude and Claude Code to 20,000+ students at community colleges, state schools, and HBCUs via CodePath.
- Anthropic board appointment: Chris Liddell was appointed to Anthropic’s Board of Directors (previously CFO of Microsoft and GM; also served as Deputy Chief of Staff during the first Trump administration).
- Anthropic funding and scaling plans: Anthropic said it raised $30B at a $380B post-money valuation, and stated the funding will deepen research, innovate in products, and expand infrastructure to make Claude broadly available.
- MiniMax distribution partnerships: Fireworks AI announced day‑0 launch partnership for MiniMax M2.5, positioning it for production agents.
Policy & Regulation
Why it matters: Competitive dynamics are increasingly spilling into policy (e.g., distillation/extraction concerns), while practical governance issues are emerging around misuse and privacy.
- OpenAI vs DeepSeek (policy warning): Bloomberg reports OpenAI warned U.S. lawmakers that DeepSeek is using “unfair and increasingly sophisticated methods” to extract results from leading U.S. AI models to train its next generation.
- Open-source jailbreak tooling debate: A discussion raises concerns about open-sourcing repositories that automate jailbreaking of open-weight models and potential misuse (e.g., weapon instructions, CSAM generation).
- Hiring/privacy concern: A purported interview practice of using AI to analyze private conversations was criticized as a “serious privacy violation,” with a note that anonymous viral claims may be unreliable.
Quick Takes
Why it matters: These are smaller items, but they often become default building blocks (datasets, eval harnesses, agent frameworks) or signal where the ecosystem is headed.
- IBM + Common Crawl: Common Crawl open-sourced annotations for IBM’s GneissWeb (a 10-trillion-token dataset) used for Granite’s core linguistic capabilities.
- Mistral Ministral 3 family: Released open-weights models (14B/8B/3B) compressed via “cascade distillation,” described as rivaling similarly sized competitors while using less training data/compute.
- Baseten replication of Generative Adversarial Distillation (GAD): Distilled Qwen3‑4B from GPT‑5.2; frames distillation as on-policy with an adaptive discriminator reward.
- DeepSpeed ZeRO load-time improvement: Tensor flattening reworked to happen on GPU (instead of CPU) to load huge multi-GPU models faster.
- Karpathy’s minimal GPT implementation: Training + inference GPT in 243 lines of dependency-free Python (core algorithm), with a separate visualization of the math-op DAG for a tiny forward pass.
Sahil Bloom
All-In Podcast
David Sacks
Most compelling recommendation: a check on “blind efficiency”
- Title: X article (title not provided) on the perils of blind efficiency (“optimize the life out of life”)
- Content type: Article (X article)
- Author/creator: Sahil Bloom
- Link/URL: http://x.com/i/article/2022361151342845952
- Recommended by: Scott Belsky
- Key takeaway (as shared): A warning about the perils of pursuing efficiency blindly—and a reminder not to “optimize the life out of life” by overlooking the hidden values in things tech increasingly does for us .
- Why it matters: It’s a clean decision-making lens for automation: efficiency gains can come with less-visible tradeoffs if the “stuff we outsource” also carried meaning, learning, or other latent value .
Related pointer: Sahil Bloom’s post linking to the same idea .
Also flagged: “Something Big Is Happening” (career opportunity for AI early adopters)
- Title: Something Big Is Happening
- Content type: Article
- Author/creator: Matt Schumer
- Link/URL: Not provided in the source clip
- Recommended by: David Sacks (referenced in conversation)
- Key takeaway (as shared): Sacks points to the piece as a viral article describing a career opportunity that will be available to “AI early adopters” .
- Why it matters: It’s a direct, high-level signal from an investor/operator that “AI early adoption” is being framed as a near-term career differentiator (at least enough to cite it as a notable viral argument) .
“And there was an article that went viral this week by Matt Schumer called Something Big Is Happening where he talked about this career opportunity that's going to be available to kind of AI early adopters.”
A useful tension across today’s two picks
Taken together, these recommendations form a practical counterbalance:
- One emphasizes opportunity for people who adopt AI early (as described in Schumer’s article, per Sacks) .
- The other emphasizes the risk of over-optimizing for efficiency, especially when tech removes activities with hidden value (as flagged by Belsky via Sahil Bloom) .
Product Growth
Product Management
Product Management
Big Ideas
1) PLG is an operating model—not a growth tactic
Product-led growth (PLG) is framed as organizing and building a company by putting the product first across acquisition, conversion, engagement, retention, and monetization .
What enables PLG to work, per the same framework:
- Strategy: CEO-level buy-in plus support from sales and marketing (called out as a common failure point) .
- Tactics: execution across the full stack of PLG levers .
- People: PMs who can drive discovery and a culture of rapid experimentation .
The 7-layer model used to structure PLG work:
- Go to market
- Information for decision
- Free-to-paid conversion
- Activation
- Retention
- Monetization
- Expansion
Why it matters: it gives PMs a shared map to diagnose where growth is actually breaking (vs. jumping to random experiments).
How to apply: start with the layer you’re “most broken” in, then address 1–2 layers below it (don’t optimize downstream levers if you lack volume upstream) .
2) Acquisition is shifting from traditional channels to product-led channels
Aakash Gupta explicitly calls out a shift from traditional marketing channels to product-led channels, and uses Canva as an example of focusing on product + user needs rather than naming competitors .
Why it matters: it changes what “good marketing” looks like for PLG—often closer to shipping product surfaces that acquire users than to classic brand spend.
How to apply: audit your top acquisition motions and ask: Is the product doing the acquiring, or are we relying on external campaigns to create demand?.
3) The “context trap”: context isn’t just transmitted—it’s produced through interaction
The Beautiful Mess argues that context is produced through interactions, not simply pooled or transmitted as background information . It contrasts a broadcast model of communication (message + background context) with approaches where alignment emerges via engagement with the situation .
"But context is produced through the interactions themselves."
Leadership implication: intent isn’t simply broadcast; it’s refined through dialogue, backbriefs, scenario exploration, and continuous adjustment—so intent becomes the context within which decisions are made . This is summarized as: “Context engineering is, in many settings, interaction design.”
Why it matters: PMs often default to “more docs / more pre-reads” as the fix for misalignment; this argues some alignment only emerges through the right interactions.
How to apply: treat key cross-functional decisions as an interaction design problem (not just a documentation problem), and choose the collaboration mechanism based on the type of context required .
4) Faster feedback loops as a moat—AI should automate the sorting, not the thinking
One PM community thesis: a defensible moat can come from a faster/better feedback loop (alongside proprietary data and distribution) . The suggested AI role is to connect into GTM systems and automatically extract and group customer questions, requests, and bugs —so PMs can spend time on user interviews and interpretation.
The same thread argues PMs should still own prioritization, interviews, PRDs, and acceptance criteria because a pipeline won’t know build effort, underlying motivations (“need behind the need”), or why customers want something .
Why it matters: it’s a concrete division of labor: use AI to remove manual tagging/search, but keep human judgment where it depends on real constraints and interpretation .
How to apply: automate intake + grouping, then explicitly reserve PM time for interviews and decision-making work that requires context and tradeoffs .
Tactical Playbook
1) Use the 7-layer PLG framework as a weekly diagnostic (not a one-time model)
Steps
- Name the broken layer (e.g., Go-to-market isn’t generating PLG leads; activation drop-off; poor conversion) .
- Work one layer up and 1–2 layers down from that failure point to find root causes and unblock testing volume .
- If layer 1 (Go-to-market) is the issue, focus on the two “you need”: marketing and website.
Why it matters: the framework warns against monetization experiments “6 layers later” when acquisition volume is the limiting factor .
2) A practical GTM checklist from the Slack vs. Canva contrast
If you’re trying to improve PLG acquisition, the examples provide a concrete way to benchmark your funnel entry.
A) Website patterns to evaluate
- Slack (2018): a “crazy simple” one-frame page with a clear positioning statement (“Where work happens”), an email CTA that uses a magic link (no password setup), client logos, and “Try it for free” emphasis .
- Canva (2026): a longer, more personalized homepage with positioning (“What will you design today?”), immediate persona-based journeys (workplace/educator/creator) mapping to plan choice, product showcase slots, template gallery, AI feature messaging, and “Sign up for free” CTA .
B) Marketing motion patterns to evaluate
- Slack (2018): traditional brand advertising (banner ads, buses, billboards, TV) positioning against “email” .
- Canva (2026): product-led SEO/AEO/GEO—creating high-volume pages that land users directly in a free tool (example: “build an Instagram post” → one click into a tool, no login, no credit card) .
How to apply: run a “first 60 seconds” audit:
- Can a new user reach value immediately (or are you gating with forms/passwords)?
- Do you personalize the journey fast enough for multiple user types to self-identify and choose a path?
- Do you have product surfaces that capture high-intent search demand and deliver value without friction?
3) Design decision-making interactions based on the type of context required
The Beautiful Mess highlights that not all decisions require the same kind of context:
- Some are rules-based (little situational awareness needed once rules are clear) .
- Some can be documented and supplied ahead of time.
- Some are emergent—context can’t be separated from the activity because it unfolds through coordination and action .
How to apply
- Classify the decision into one of the three context types above .
- If it’s emergent, don’t over-rely on “broadcast context.” Instead, use mechanisms like dialogue, backbriefs, scenario exploration, and continuous adjustment so alignment emerges through engagement .
- Treat the PM’s job here as “interaction design” (designing the process that produces shared context) .
Case Studies & Lessons
1) Slack’s 2018 PLG entry point: remove setup friction and get users into the product
Slack reportedly grew from 0 to 8 million daily active users in 4 years. The GTM mix highlighted includes traditional advertising positioning against email and a homepage designed to drop users into the product quickly via email + magic link (no password setup) .
Lesson for PMs: if activation depends on getting users into a workspace quickly, the website/onboarding experience is a product surface—not just marketing collateral.
What to copy: evaluate whether your initial CTA removes steps (like password creation) and gets users to an “aha” state with minimal delay .
2) Canva’s 2026 playbook: product-led acquisition plus user-centric messaging
Canva is cited at 260M monthly active users, $3.5B ARR, and 40%+ growth per year. The playbook emphasizes:
- Product-led SEO/AEO/GEO that routes high-intent searches into a free tool experience with no login and no credit card.
- A homepage that immediately personalizes by user type and showcases templates and AI capabilities, with “Sign up for free” as the CTA .
A specific positioning lesson: Canva is described as “stealing $2B ARR from Adobe,” but Adobe wasn’t mentioned on Canva’s website—“focus on the product and your user” .
Lesson for PMs: rather than leading with competitor comparisons, lead with a user need and a fast path to value.
What to copy: build acquisition surfaces that deliver value in one click, and align the homepage around user-specific paths (personas → plan fit) .
Career Corner
1) Competing with internal candidates for a platform PM role: de-risk ramp + show platform ownership
In a thread about interviewing against internal candidates for a role building an internal UI platform/framework across web, iOS, and Android, the core role requirement is interviewing developers and building for them—where engineering fluency and trust matter .
A key framing: internal vs external tradeoffs are often speed/context/relationships (internal) versus pattern recognition and technical credibility (external); the role described is said to lean toward the second bucket .
How to apply in interviews
-
Reduce perceived risk in two areas:
- Show you can ramp on internal context quickly (examples: map stakeholders, find early adopters, ship something useful before full alignment) .
- Demonstrate you think like a platform owner (adoption, trust, backwards compatibility, long-term maintenance) .
- With a Director of Engineering: emphasize partnership (translate developer pain into product decisions, handle pushback, balance consistency with team autonomy) to be seen as a peer—not a requirements collector .
- One commenter argues “internal context” is less important than product/tech knowledge plus a proven track record; the interview job is to convince a small set of people you could do the work quickly .
2) Big tech → startup: recognize the explicit tradeoffs, then validate the environment
A PM considering leaving big tech after a 4-year cliff cited a potential 25% compensation bump and more interesting product work, weighing it against WLB impact and risk .
Community responses emphasized:
- Assess work environment and founders’ experience; some startup environments can be “too difficult to work in” .
- Another commenter who quit big tech to start a company said it was “totally worth it,” and argued the equation is tilted toward startups “right now” (linked to their outlook on AI), with the comp bump making it sweeter .
How to apply: treat founder experience and day-to-day environment as first-order diligence inputs—not afterthoughts .
Tools & Resources
- PLG framework and examples (Aakash Gupta): “How to build product-led growth in 2026 (the complete 7-layer playbook)”https://www.news.aakashg.com/p/plg-in-2026
- Product-led SEO/AEO/GEO link referenced in the PLG post: https://www.news.aakashg.com/p/guide-aeo-geo
- The Beautiful Mess on the context trap: https://cutlefish.substack.com/p/tbm-406-seeing-everything-understanding
- Shannon–Weaver model (linked in the context piece): https://en.wikipedia.org/wiki/Shannon%E2%80%93Weaver_model
- Reddit: Platform PM interview thread (internal vs external): https://www.reddit.com/r/ProductManagement/comments/1r3vfpb/
- Reddit: Faster feedback loops + AI grouping idea: https://www.reddit.com/r/ProductManagement/comments/1r3ifeq/comment/o54l3pz/
- Reddit: Big tech → startup tradeoffs: https://www.reddit.com/r/ProductManagement/comments/1r4d4bs/
- Documentation pain point: a post notes documentation is still manual even with GPT and claims a free way to generate product guides with automatic screenshots and video demos “in like… 40 seconds” (via DM to the author)
Angie Setzer
Dept. of Agriculture
Successful Farming
Market Movers
Grains & oilseeds (U.S. + South America)
- Soybeans: March ’26 futures closed near $11.37/bu (+~13¢), with strength tied to reports the U.S.–China trade truce could be extended by up to a year and renewed talk of China lifting total U.S. soybean purchases to 20 MMT. At the same time, weekly export sales hit a marketing-year low at 11M bushels, down 36% WoW and 80% vs. the prior 4-week average .
- Corn: USDA raised U.S. corn exports to a record 3.3B bushels (+100M) and lowered ending stocks to ~2.1B bushels. Weekly net corn sales were reported at 81M bushels (up 99% WoW) and export pace was described as still ahead of the level needed to hit USDA’s higher projection .
- Wheat: USDA showed wheat feed/food use down 5M bushels, lifting stocks to 916M in one set of highlights . Weekly net wheat sales were 18M bushels (up 31% WoW), led by the Philippines.
Livestock (U.S.)
- Cattle: 5-market fed steer average was $241/cwt through Thursday (flat) with expectations it could finish higher . February live cattle futures ended the week at $242.93/cwt (up >$5 WoW) .
- Beef/pork values: Choice boxed beef was $364.47/cwt (down $4.23 WoW) and Select $363.42/cwt (down $0.41) . National base hog carcass price was $85.35/cwt (+$0.47) while pork cutout was $94.87/cwt (down $0.15) .
Trade & policy signals affecting demand
- U.S.–China (soy focus): Soybean prices were repeatedly tied to optimism that the trade truce may be extended and that it could support additional Chinese buying .
- U.S.–Taiwan deal: A finalized agreement was reported to cut tariffs and expand market access in Asia, including U.S. beef, dairy, pork, and wheat, alongside Taiwan’s pledge to buy >$44B in LNG and crude oil . Related coverage noted livestock/dairy/trade groups praising the agreement for expanding market access .
- Beef imports (U.S.): Following an executive order, the U.S. will temporarily expand beef imports from Argentina, raising the tariff-rate quota for lean beef trimmings by 80,000 MT for 2026, starting Feb. 13, with the stated goal of easing ground beef prices amid historically high costs and a shrinking U.S. cattle herd .
Innovation Spotlight
Vertical integration + AI in produce packing (U.S.)
Alsum Farms and Produce (Wisconsin) was highlighted for controlling growing, packing, washing, sorting, and distribution of millions of pounds of potatoes, pumpkins, and produce annually . The operation is using AI for sizing/sorting to improve speed and efficiency .
Special fertilizers and organo-mineral formulations (Brazil)
- Brazil’s special fertilizers segment grew about 19% in 2024, with expectations of continued growth through 2026 .
- Coverage emphasized these products are intended to complement, not replace, traditional fertilizers—aiming to improve nutrient-use efficiency and reduce losses .
- Organo-mineral production examples included converting leather-derived residues via hydrolysis into inputs providing amino acids and organic nitrogen within fertilizer formulations .
Precision livestock operations with measurable controls (Brazil)
A Mato Grosso do Sul swine operation expanded from 2017 into 3 nuclei / 12 barns, with licensed capacity up to 33,000 pigs. Tech and management elements highlighted included solar panels, biodigester infrastructure, and a robot to aid animal counting, plus weekly KPI monitoring for animal well-being, conversion, and mortality .
Regional Developments
U.S.: drought and early-season weather context
- The Corn Belt saw little to no rainfall over the last week in one update; drought conditions were largely unchanged overall but worsened in parts of Illinois, Iowa, Missouri, and across portions of Nebraska and Kansas.
- Reported drought exposure by area: corn country 31%, soybeans 37%, winter wheat 45%, spring wheat 11%, cattle country 39%.
- Another outlook described a warm stretch (highs in the 60s–70s) across the Plains/Midwest, while dry and drought conditions remained a concern for winter wheat heading toward spring .
Brazil: storms, harvest windows, and export constraints
- Severe storms were forecast across southern Brazil (RS/SC/PR), including hail, winds >100 km/h, and potential tornado/microburst conditions—posing risk to field operations and harvest activity .
- In central production areas, producers were urged to use a near-term window to harvest soybeans and finish planting second-crop corn and cotton before heavier rain (up to ~100 mm in 5 days) disrupts operations .
- Soy harvest progress: Conab cited ~17.4% harvested across the 12 main producing states, with Mato Grosso at 46.8%.
Brazil–China beef quota (Brazil)
China’s safeguard quota for Brazil was reported at ~1.06M tons, and January shipments were >119k tons (~11% of quota), reflecting an early-year rush by exporters/importers to secure quota share . At the current pace, quota exhaustion by Aug/Sep was flagged, after which exports would face an additional 55% tariff viewed as making exports to China largely unviable .
Best Practices
Weed control: focus on multiple modes of action + residual layering (U.S.)
- Corteva’s Eric Schurter emphasized building herbicide programs around multiple effective modes of action (at least two) and layering residuals to maintain control through canopy closure .
- For soybeans, he noted >65% of U.S. soybean acres are Enlist E3, and stressed pre-plant burndown / pre-emergence execution because many tools are pre-only and earlier planting extends the time residuals must last .
Bringing CRP/pasture back into crop production (U.S.)
Ag PhD’s guidance prioritized:
- Soil testing first, recommending grid sizes ≤5 acres to capture variability .
- Preserving built organic matter by avoiding unnecessary full-scale tillage, while still acknowledging some tillage may be needed due to rodents/erosion .
- Using Roundup-tolerant soybeans to control perennial grasses, or planning extra nitrogen if planting corn/wheat due to high-carbon residue .
Marketing posture noted by analysts (U.S.)
One set of producer guidance suggested rallies remain selling opportunities in an oversupplied global environment, including a recommendation to consider forward-selling 40–60% of the 2026 crop and “reward the rallies” .
Input Markets
Fertilizer: nitrogen tightness + high phosphate ratios (global)
- Urea values were reported climbing amid U.S.–Iran tensions, Europe running at ~75% of normal production, and uncertainty around China’s 2026 export plans .
- DAP in New Orleans was cited around $625, with December 2026 corn at $4.56 (ratio ~137) .
- Potash was described as the only major input still “well priced,” with manufacturers keeping values steady relative to grain .
Input-cost policy + insurance structure (U.S.)
A Farm Journal segment described crop insurance changes tied to the “One Big Beautiful Bill,” including higher premium subsidies at several coverage levels and a shift in how payment limits apply for LLCs/S corporations (now based on the number of equal owners) .
Forward Outlook
Key dates and planning items
- China trade timeline: Markets were described as tuned to President Trump’s scheduled April China visit , with sources emphasizing that a signed agreement would provide needed certainty for the market .
- USDA Outlook Forum: Outlook Forum figures were flagged as coming next week.
- USDA data confidence: A Farm Journal survey reported eroding confidence in USDA reporting—68% of economists, 73% of producers, and 78% of retailers said they’re less confident than in the past . NASS has launched an internal review after market-moving revisions to 2025 corn acreage estimates .
Farm financial backdrop (U.S.)
- USDA’s 2026 net farm income forecast was $153.4B (down 0.7% vs. 2025) and net cash farm income $158.5B (up 3%) . Direct government payments were forecast to rise ~$14B in 2026 while commodity cash receipts were forecast to fall ~$14B.
- Farm Bureau cited 315 Chapter 12 bankruptcies in 2025 (+46% vs. 2024) .
Active USDA programs (deadlines + totals)
- USDA announced $1B in assistance for specialty crop and sugar producers impacted by unfair market disruptions; reporting 2025 acres to FSA is due March 13, 2026. Details were linked in USDA’s release: https://www.usda.gov/about-usda/news/press-releases/2026/02/13/usda-announces-assistance-specialty-crop-farmers-impacted-unfair-market-disruptions.
- USDA issued final ELRP payments totaling >$1.89B (including 100% lump-sum payments for eligible flood/wildfire assistance applicants) .
- USDA also announced >$212M via the Market Access and Foreign Market Development programs to support export market development by farm groups .
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Sam Altman
3Blue1Brown
Paul Graham
The Pragmatic Engineer
r/MachineLearning
Naval Ravikant
AI High Signal
Stratechery
Sam Altman
3Blue1Brown
Paul Graham
The Pragmatic Engineer
r/MachineLearning
Naval Ravikant
AI High Signal
Stratechery
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Deedy
Windsurf
Armin Ronacher
🔥 TOP SIGNAL
Warp’s Zach Lloyd is shipping the next layer of agent infrastructure: OZ, a cloud orchestration platform meant to move “too many agents on a laptop” into a managed, team-visible system (search, filters, PR tracking, conversation history) . The practical shift: once you’re running fleets of agents, visibility + coordination becomes the bottleneck at least as much as model quality .
🛠️ TOOLS & MODELS
Warp — OZ (new launch): Cloud agent orchestration platform positioned as “Vercel for agents,” aimed at teams outgrowing laptop CPU/RAM and needing centralized tracking/visibility .
- Usage signal: Warp users are launching ~3M agents daily.
- Business pace signal: first $1M ARR took ~1 year, now adding $1M every 5–6 days.
Zach Lloyd’s coding model pick (hands-on)
- Preferred model “as of last week”: Codex 5.3; previously Opus 4.6.
- Why: Codex 5.3 does better on “very complex hard problems” (at the cost of speed); he reports more context loss and partially-thought-through solutions with Opus 4.6 .
- Warp is model-routed: they plan to auto-pick models based on a pass over task complexity (no manual “rotation” by engineers) .
Codex CLI (Windows) — agent sandbox
- Codex announced “the world’s first coding agent sandbox for Windows,” enabling agents to run without approving specific commands.
- Requires Codex CLI v0.100.0+.
- Roadmap mentioned: IDE extension + Windows Codex app .
- Shared by Greg Brockman linking to the announcement .
Codex app — feature drop + Windows invites
- New: 5.3-codex-spark, forking, pop-out window, mark unread, perf/quality improvements (+ “a secret fun thing”) .
- “Tomorrow: first Windows alpha invites” .
- Pop-out window called out as useful for iterating alongside the browser / not losing context across workspaces .
Cursor — long-running agents (practitioner mention): Hosts report Cursor “long running agents” that can run “for hours” just released .
OpenClaw (beta) — v2026.2.13
- “Chunky” beta update shipped .
- Dev-reported improvements: tests ~2x faster and faster CLI load.
- PR counter: ~2700 → 2722.
CodexBar — v0.18.0-beta.3: New release for tracking token usage.
Windsurf — Arena Mode (model choice in the loop)
- Format: one prompt, two model outputs, you vote; positioned as a “real-world coding” benchmark vs traditional benchmarks .
- Free “for the next week” (post-launch) .
- @swyx’s reported outcome: “total anthropic victory” .
Benchmarks vs tool reliability (Gemini discourse)
- Reported: Gemini 3 Deep Think scored 3455 on Codeforces (claimed equivalence: “#8 best competitive programmer”), vs prior best 2727 for OpenAI o3 (claimed “#175”) .
- Theo’s usability critique: Gemini 3 Pro is “as smart as Opus 4.6” but “screws up tool calls” consistently .
Cline CLI 2.0 — Go → TypeScript rewrite (shipping reality)
- Team rewrote a bug-heavy CLI from Go to TypeScript to reuse existing TS code; result: “sleeker and usable,” enabling seamless agent evals in the CLI .
- Theo’s take: “go is a terrible language for CLIs” .
💡 WORKFLOWS & TRICKS
Run agents like a team, not a tab (OZ pattern)
- Problem: lots of agents on laptops → CPU/RAM/file space constraints, plus no centralized view of what’s happening .
- OZ’s approach: a web app showing all agents across a team; search/filter by PRs; record of conversations .
Expect “attention saturation” as your first scaling limit
- When you can’t context-switch fast enough, “we are the limiter of our total pipeline of work” → idea: an orchestrator agent overseeing other agents .
Terminal-as-control-plane for agents (vs IDE)
- Pitch: prompting in English replaces many command invocations, but you still want a record of what ran, plus multiple panes/sessions—making a terminal-like interface a better form factor than a “word processor interface” IDE .
- Warp’s “middle ground”: markdown viewer, file tree, native diff viewer → described as an “agentic development environment” .
Parallelize safely: split by non-overlap (and keep context clean)
- Armin Ronacher’s rule: don’t parallelize conflicting work; use sub-agents for research tasks that don’t change files; separate work by folder / backend vs frontend / git worktrees to avoid merge conflicts and reduce review overhead .
Structure-first, then delegate (repeatable “handoff” pattern)
- Start a new project by writing core structure/architecture by hand, then gradually give the agent more responsibility .
- Keep the most critical slice handwritten: “5–10% … really important crucial bit” gets more manual attention .
Write “dumber” code (fewer abstractions) to make agent + human debugging faster
- Argument: lots of abstraction layers increase time-to-fix during outages; LLMs are not good at abstractions but are good at straightforward solutions .
- Concrete example: choose raw SQL over an ORM—agents can write SQL, making the trade-off less painful .
Sandboxing and “YOLO flags” are converging on the same question
-
Simon Willison asks whether people run
--yolo(Codex) /--dangerously-skip-permissions(Claude Code), and if they YOLO in a sandbox . - Codex’s Windows sandbox pitch: let the agent work without per-command approvals (within a sandboxed environment) .
-
Simon Willison asks whether people run
Operational safety: avoid insecure third-party OpenClaw setup services
- Warning from a tester: multiple setup services allegedly exposed the gateway, lacked pairing mode, and allowed internet discovery of the root directory—“DO NOT use” .
- Maintainer agrees: quick installers may skip encryption and not surface security docs .
Two “update/install via curl” patterns (use with eyes open)
-
OpenClaw beta update instructions: ask your agent to update to beta or run
curl -fsSL https://openclaw.ai/install.sh | bash -s – –beta. -
TinyClaw (alternative approach): install with a remote shell script then
tinyclaw start.
-
OpenClaw beta update instructions: ask your agent to update to beta or run
👤 PEOPLE TO WATCH
Zach Lloyd (Warp) — clear-eyed about where agents work today vs where they don’t (and why teams still hire senior engineers).
“Coding is not close to being solved… for hard, complicated software.”
Armin Ronacher (@mitsuhiko) — strong practitioner heuristics (tool loop, low abstraction code, parallelization) + sharp product ops.
- Disabled GitHub Copilot in his enterprise org until it improves .
Peter Steinberger (@steipete) — shipping fast on OpenClaw + calling out security footguns.
- Also ships tooling around agent usage: CodexBar for token tracking .
Alexander Embiricos (@embirico) — frequent Codex shipping notes; Windows sandbox + Codex CLI requirements are the kind of detail that matters in real workflows .
@arafatkatze / Theo — showing the unglamorous work (rewrites, UX bugs, eval plumbing) needed to make agent tooling actually usable .
🎬 WATCH & LISTEN
1) Warp launches OZ: “Vercel for agents” + team visibility (≈1:01:39–1:04:03)
Hook: A concrete picture of what breaks when everyone runs agents locally—and the primitives (search, PR filtering, conversation records) you need once agents become a shared team resource .
2) Why agents don’t replace engineers (yet): supervision + hard codebases (≈1:10:42–1:14:11)
Hook: Lloyd explains why Warp still hires engineers: agents need supervision, and “hard” product code (custom UI frameworks, bug-prone changes) punishes unsupervised automation .
3) “Dumb code” wins: fewer abstractions, faster recovery (≈0:11:55–0:15:22)
Hook: Ronacher’s argument for writing low-abstraction code (and even using raw SQL) because both humans and LLMs debug it faster when production breaks .
📊 PROJECTS & REPOS
- OpenClaw v2026.2.13 (beta release) — https://github.com/openclaw/openclaw/releases/tag/v2026.2.13
- OpenClaw beta install script — https://openclaw.ai/install.sh
- TinyClaw (OpenClaw-like in ~400 LoC) — https://github.com/jlia0/tinyclaw
- TinyClaw remote installer — https://raw.githubusercontent.com/jlia0/tinyclaw/main/scripts/remote-install.sh
- CodexBar v0.18.0-beta.3 — https://github.com/steipete/CodexBar/releases/tag/v0.18.0-beta.3
- Cline CLI 2.0 link — https://x.com/cline/status/2022341254965772367
- Windsurf Arena Mode post — https://x.com/windsurf/status/2017334552075890903
— Editorial take: We’re exiting the “which model is best?” phase and entering the ops phase: sandboxes, orchestration, visibility, and human attention are becoming the real limiting factors.
Anthropic
Dario Amodei
Ben Thompson
Frontier science: OpenAI showcases physics progress + a new “frontier proof” benchmark
GPT-5.2 contributes a new theoretical physics result (gluon amplitudes)
OpenAI says GPT-5.2 helped derive a new result in theoretical physics: a “single-minus” gluon interaction at tree level long treated as having zero amplitude can be non-zero in a carefully defined particle-alignment scenario . The work is being released as a preprint with collaborators from IAS, Vanderbilt, Cambridge, and Harvard, and OpenAI is soliciting feedback as it submits for publication .
Why it matters: This is a concrete claim of AI-assisted progress on a long-assumed “empty” case in amplitude theory, plus an explicit methodology report (simplification → conjecture → independent proof → author verification) .
Links: OpenAI post https://openai.com/index/new-result-theoretical-physics/ • arXiv https://arxiv.org/abs/2602.12176
Physicists’ reaction: “might not have been solvable by humans”
Greg Brockman relayed a conversation with physicists Andrew Strominger and Alex Lupsasca, describing an internal-model run that “solved AND proved a previously unsolved problem in quantum field theory…in 12 hours” . Strominger is quoted as saying:
“It is the first time I’ve seen AI solve a problem in my kind of theoretical physics that might not have been solvable by humans.”
Brockman also attributes the step-change to both model improvements and learning “how to talk to it,” adding Strominger’s view that many physicists may need to learn to interact with these systems to keep up with the frontier .
Why it matters: Beyond the preprint, this reflects a qualitative shift in how domain experts describe AI’s ceiling in their own research practice—especially for problems that are traditionally hard to verify quickly .
OpenAI launches “First Proof” to benchmark novel math research
OpenAI is now benchmarking models on novel frontier research via the “First Proof” challenge (http://firstproof.org) . In a week-long “side-sprint” with limited human supervision, an internal training model produced solutions judged “likely correct” for at least 6 of 10 unpublished problems (2, 4, 5, 6, 9, 10), though OpenAI notes the problems are difficult to verify and relied on expert feedback .
Why it matters: It’s an explicit move to evaluate models on new research targets (not just benchmark corpora), while also foregrounding the verification bottleneck as part of the measurement problem .
Open-source + product releases: agents, biology, and world-building
MiniMax open-sources “MiniMax-M2.5,” trained via RL in complex environments
MiniMax announced it has open-sourced MiniMax-M2.5, trained with reinforcement learning across “hundreds of thousands of complex real-world environments,” and claims state-of-the-art performance in coding, agentic tool use, search, and office workflows .
Why it matters: This is another step in the “closed vs. open” convergence narrative—amplified by Emad Mostaque’s claim that MiniMax is “closing the closed-open source frontier AI gap” .
Links: https://huggingface.co/MiniMaxAI/MiniMax-M2.5 • https://github.com/MiniMax-AI/MiniMax-M2.5
ByteDance releases Protenix-v1 for biomolecular structure prediction
A post shared on r/LocalLLM says ByteDance released Protenix-v1, an open-source model for biomolecular structure prediction described as achieving “AF3-level performance” .
Why it matters: Open releases in bio/structure prediction keep accelerating—expanding the set of serious, locally-runnable tools in a domain where model access and reproducibility matter.
Repo: https://github.com/bytedance/Protenix
Google DeepMind opens Project Genie world-building to U.S. AI Ultra subscribers
Google DeepMind announced Project Genie, framing it as a system that “built” explorable worlds from what users “dreamed” . It’s available for creation to U.S. Google AI Ultra subscribers via labs.google (Project Genie) .
Why it matters: World-generation is moving from demos to consumer-accessible creation flows—packaged as an interactive “world” product, not just a video output .
Link: https://labs.google/projectgenie
World Labs expands advanced 3D-world editing controls in “Marble”
World Labs says advanced editing is now available for all users, and Fei-Fei Li highlighted the new “Advance model” as providing more editable control when creating “a true 3D world” (e.g., changing room vibe, adding scenes outside windows, generating variations) .
Why it matters: The workflow emphasis is shifting toward iterative, controllable world-building (image → pano → edit → create world), not just one-shot generation .
Scaling economics + governance: Amodei on compute risk, diffusion, and regulation
Dario Amodei: demand prediction makes datacenter scaling financially perilous
In a recent interview, Anthropic CEO Dario Amodei argued that buying datacenters is risky because if you’re “off by a couple years,” it can be “ruinous” . He describes uncertainty around how quickly technical progress turns into revenue, and lays out why over-buying compute based on aggressive growth assumptions could drive bankruptcy if growth slows meaningfully .
Why it matters: It’s a clear articulation of why even teams with aggressive “powerful AI” timelines may still scale compute conservatively: diffusion and demand forecasting are constraints, not just engineering ambition .
Amodei calls for federal standards (and rejects a 10-year state moratorium)
Amodei criticized the idea of banning state AI regulation for 10 years without a concrete federal plan, calling 10 years “an eternity” given risks like bioterrorism and autonomy . He said he could support federal preemption in the form “here’s our standard; this applies to everyone,” and emphasized near-term urgency around transparency standards and targeted regulation if risks become clearer .
Why it matters: This is a specific regulatory posture from a leading lab: allow state action absent federal movement, but prefer a coherent federal baseline once feasible .
Anthropic expands Claude access via CodePath
Anthropic announced a partnership with CodePath to bring Claude and Claude Code to 20,000+ college students (community colleges, state schools, HBCUs) .
Why it matters: Distribution deals aimed at early-career developers are becoming a strategic channel—especially as coding agents become a core wedge product.
Link: https://www.anthropic.com/news/anthropic-codepath-partnership
Safety, trust, and geopolitics: xAI turmoil, “Adult Mode,” and hallucinations
Reported xAI restructuring includes safety-team concerns
Gary Marcus reposted reporting that former xAI employees described restructuring tensions over safety and being “stuck in the catch-up phase” . Marcus also quoted a claim that “Safety is a dead org at xAI” and noted that Musk’s shared org chart contained no mention of a safety team .
Why it matters: This is a notable governance signal: safety org structure (and perceived deprioritization) is becoming a public, competitive, and reputational factor .
Grok’s growth is tied to engagement dynamics (including sexual content)
A Big Technology report says Grok’s U.S. daily chatbot-app market share rose from 1.6% → 15.2% (Jan 2025 to Jan 2026), trailing only ChatGPT and Gemini . It also reports Grok’s user base is 82% male and highlights sexually oriented companion features and training processes involving reviewing sexual conversations (as reported by The Washington Post) .
Why it matters: It frames “companion” and adult content as an engagement lever in the chatbot app race—alongside safety and brand risk (including a reported spike tied to safeguards lapses) .
OpenAI’s reported plans for “Adult Mode” add pressure to the engagement race
The same report says OpenAI is planning an “Adult Mode” in ChatGPT in the coming months .
Why it matters: If accurate, it underscores how growth pressures can expand product scope into more controversial interaction modes—raising new policy, safety, and trust questions .
Marcus argues hallucinations remain a major real-world constraint
Marcus argued that hallucinations are still prevalent and subtle, citing: a rise in lawyer incidents involving fake cases (from ~100 to 900+ in under a year), an AIMultiple benchmark showing 15%+ hallucination rates across models, and a reported pharma study finding 26%–69% hallucination rates on challenging problems . He also cited a “Remote Labor Index” result that AI completed only 2.5% of sampled online human tasks .
Why it matters: Even amid capability breakthroughs, reliability remains a binding constraint for adoption—especially in high-stakes domains (law, pharma) .
Pentagon briefly adds (then removes) Alibaba/Baidu from a military-aid list
A r/LocalLLM post says the U.S. Pentagon added Alibaba, BYD, Baidu, and TP-Link to a list of companies “aiding the Chinese military,” then removed them minutes later without explanation—triggering stock drops and investor concern . Alibaba and Baidu denied military ties and said they focus on civilian AI applications .
Why it matters: Even transient designation events can move markets and intensify U.S.–China tech friction—relevant for AI supply chains, partnerships, and capital access .
Competing narratives: ads, AGI pace, and Hollywood’s response
Ben Thompson criticizes Anthropic’s Super Bowl messaging about “ads in answers”
In a Sharp Tech episode, Ben Thompson called Anthropic’s Super Bowl ad “despicable” and “lying,” arguing it depicted OpenAI inserting ads into responses even though OpenAI is not doing that . He characterized the move as “strategy credit,” arguing Anthropic is enterprise-focused and attacks an ad business it likely won’t build .
Why it matters: The “ads in assistants” debate is now also a brand war—and one that may influence talent, not just users .
Chollet: AGI won’t necessarily cause a sudden capability explosion
François Chollet argued that AGI’s rise won’t lead to a “sudden exponential explosion” in capabilities because of bottlenecks in sources of improvement, and that scaling intelligence in silicon doesn’t remove those bottlenecks . He framed AGI as part of a longer, essentially linear arc of scientific progress over centuries, not a single discontinuity .
Why it matters: It’s a prominent counterpoint to “takeoff” framing—useful context as more labs publicize frontier-science results and aggressive timelines .
Andrew Ng: Hollywood is anxious—but there’s common ground
After speaking at Sundance, Andrew Ng described Hollywood’s discomfort with AI companies learning from creative works without consent/compensation, and unions’ concerns about job displacement (e.g., SAG-AFTRA) . He argued there’s common ground around guardrails against deepfakes and upskilling, and said AI video tools could make creation easier for millions if developers and Hollywood collaborate .
Why it matters: Entertainment remains a high-signal arena for AI’s IP, labor, and deepfake debates—where “alignment” questions quickly become contract and governance questions .
Bloomberg
Andrej Karpathy
Kling AI
Top Stories
1) GPT‑5.2 helped derive (and prove) a new theoretical-physics result on gluon amplitudes
Why it matters: This is a concrete example of an LLM contributing to frontier research: simplifying previously intractable expressions, proposing a general formula, and supporting a proof workflow that the authors then verified—while also challenging a long-standing “zero amplitude” assumption in quantum field theory.
- What the preprint claims: A gluon interaction (“single-minus” at tree level) long treated as having zero amplitude can be non-zero under a carefully defined alignment condition.
- How it was found: The authors had computed results up to n=6 gluons by hand; GPT‑5.2 simplified the expressions and conjectured a general formula; a separate scaffolded internal model independently derived the same formula and produced a formal proof in about 12 hours; the authors verified the result.
- Implications noted by OpenAI: Finding structure in a case long thought empty “sharpens our understanding” and “opens new directions,” including extensions to gravity and related amplitude relations.
“It is the first time I’ve seen AI solve a problem in my kind of theoretical physics that might not have been solvable by humans.”
References: OpenAI write-up https://openai.com/index/new-result-theoretical-physics/ and arXiv https://arxiv.org/abs/2602.12176.
2) “First Proof” raises the bar for evaluating research-level math capability
Why it matters: Several discussions argue that novel frontier research problems are a more meaningful capability test than standard benchmarks—especially when problems are expert-domain and solutions are hard to verify quickly.
- The First Proof benchmark consists of 10 math research problems that research mathematicians have solved but not published; teams had a week to attempt solutions with LLMs.
- An internal model run with limited human supervision produced solutions that (based on expert feedback) have a high chance of being correct for at least 6/10 problems (2, 4, 5, 6, 9, 10).
- Method notes: one-week side sprint; no proof ideas given; some solutions expanded per expert feedback; manual back-and-forth with ChatGPT for verification/formatting; best attempts selected.
Site: http://firstproof.org.
3) Microsoft signals intent to build its own frontier foundation models
Why it matters: If Microsoft shifts more of its stack toward in-house foundation models, it could reshape the competitive dynamics of model access, distribution, and “default” enterprise AI infrastructure.
- In an FT interview (per reporting), Microsoft AI chief Mustafa Suleyman said: “We have to develop our own foundation models, which are at the absolute frontier, with gigawatt-scale compute and some of the very best AI training teams in the world.”
4) SWE-rebench: token budget and trace efficiency are now first-class constraints
Why it matters: For coding agents, capability is increasingly entangled with how effectively an agent spends its token budget across long tool-using trajectories—not just single-shot model quality.
- SWE-rebench’s January update highlights a ~1M token wall: beyond ~1M tokens per problem, additional tokens yield only marginal pass@1 improvements.
- The benchmark evaluates real-world SWE tasks in an iterative agent loop (read files → patch → run tests → refine), where token counts reflect the full trajectory.
- A “top cluster” (Claude Code, Claude Opus 4.6, gpt‑5.2‑xhigh) operates in the ~1–2M tokens/problem regime.
- Efficiency note: gpt‑5.2‑codex is called out as a notable exception—performing strongly below ~1M tokens/problem.
Separately, SWE-rebench reported Jan–Feb numbers with 46 new PRs, with Opus 4.6 and Codex 5.3 leading at 51.7% resolve rate.
5) MiniMax M2.5 continues to spread through agent tooling—alongside mixed reliability signals
Why it matters: A cheap, fast open(-ish) model can change what’s economically feasible for long-horizon agents, but real-world adoption will depend on reliability tradeoffs (e.g., hallucination and instruction following).
- MiniMax positions M2.5 as an open-source model trained via RL across hundreds of thousands of complex real-world environments, targeting coding/tool use/search/office workflows.
- Artificial Analysis describes M2.5 as an incremental upgrade over M2.1 (+2 Intelligence Index points to 42) with improved agentic performance (GDPval-AA ELO 1215 from 1079), but also a higher hallucination rate (reported at 88%, with AA-Omniscience Index dropping to -41 from -30).
Research & Innovation
Why it matters: This cycle’s technical work clusters around (1) better RL/post-training recipes, (2) architectures and systems to make long-horizon/long-context cheaper, and (3) data curation methods that scale beyond “one score.”
RL + post-training recipes and theory
- MaxRL (CMU/Tsinghua/Zhejiang/UC Berkeley): A sampling-based framework meant to bridge standard RL and exact maximum likelihood; reported to Pareto-dominate existing methods with up to 20× better test-time scaling efficiency than GRPO. Paper: https://arxiv.org/abs/2602.02710
- Length-Incentivized Exploration (LIE): RL recipe for test-time reasoning that uses a length reward plus a redundancy penalty to address the “Shallow Exploration Trap,” improving in-domain math and out-of-domain tasks; includes AIME25 gains (20.5% → 26.7%). Paper: https://arxiv.org/abs/2602.11748
- DPPO (Divergence PPO): Replaces token-ratio clipping with a full-distribution divergence constraint (TV/KL) to control harmful updates; claims include higher final rewards and stability without extra tricks. Paper: https://arxiv.org/abs/2602.04879
- RLER (RL with Evolving Rubrics) / Rubrics-as-Rewards: Maintains a buffer of per-prompt rubrics (seeded with search-grounded rubrics) and evolves them to stay discriminative as the policy shifts, including adding negative rubrics to target reward hacking. Paper: https://arxiv.org/abs/2511.19399
Efficient architectures and long-context behavior
- Transformer–SSM hybrid with 2% attention heads: Reports that scattering only “a single layer’s worth” of attention heads across the network can recover 95%+ of full Transformer performance on recall, math, and more; introduces Retrieval-Aware Distillation to keep only retrieval-performing heads, compressing hybrid state size by 5×. Paper: https://arxiv.org/abs/2602.11374
Data curation for multimodal and training mixes
- SkillRater (Perceptron.inc): Argues multimodal data quality is multidimensional; decomposes filtering into capability-aligned raters with near-orthogonal signals, reporting consistent improvements over monolithic scoring. Paper: https://arxiv.org/abs/2602.11615
- Olmix (Allen AI): Frames data mixing (web/code/math ratios) as a first-order lever on model quality and introduces a framework to configure and update mixing methods as datasets change during development.
Training/serving and model-building automation
- Custom CUDA kernels via an agent: Reports an agent that writes kernels tailored to a specific model + hardware instruction set, using benchmarks as rewards; kernels worked with Diffusers and
torch.compileand delivered speedups without quality loss (per the post). - vLLM on GB300: Reports DeepSeek R1 on GB300 achieving 22.5K prefill TGS and 3K decode TGS per GPU, described as an 8× prefill and 10–20× mixed-context improvement over Hopper; includes a recipe using NVFP4 + FlashInfer FP4 MoE kernel.
Products & Launches
Why it matters: Model improvements are increasingly “real” only when paired with distribution, integrations, and agent runtimes that users can adopt quickly.
Models and access
- GPT‑5.2 rollout + public testing: GPT‑5.2 is rolling out to everyone and is available in the Arena Text and Vision leaderboard for battle-mode testing; Arena notes the API name “gpt-5.2-chat-latest.”
- MiniMax‑M2.5 open-source distribution: Available on Hugging Face and GitHub, with day-0 ecosystem support (vLLM, and deployment partners).
Coding agents and dev workflows
- Cline CLI 2.0: Open-source coding agent running fully in the terminal, adding parallel agents, headless CI/CD support, and ACP support for multiple editors.
- Windows coding-agent sandbox: A “world’s first coding agent sandbox for Windows” claims safer agent operation without approving each command; live in the CLI with IDE extension and Windows Codex app planned.
- WebMCP starter template: Demonstrates agents interacting with websites without “seeing” the UI (browser-as-API), including a DoorDash-like checkout flow. Repo: https://github.com/Doriandarko/webmcp-starter
Multimodal creation and video
- Kling 3.0 in Video Arena: Positioned as an all-in-one multimodal creation engine; now testable in Arena’s video battle mode.
- Seedance 2.0 + agents: Chatcut reports Seedance 2.0 working with the OpenClaw agent to generate a UGC product video from an Amazon link (crawl page → extract info/photos → feed assets to Seedance).
- Google DeepMind Project Genie: Released for U.S. Google AI Ultra subscribers via https://labs.google/projectgenie
Platform tooling and observability
- Gemini Interactions API multimodal function calling: Tools can return actual images (not text descriptions), and Gemini 3 can process them natively; results can mix text + image.
- PostHog LLM Analytics + LlamaIndex integration: Tracks OpenAI usage (tokens, cost, latency) for a demo agent workflow.
- Stirrup agent harness speed tracking: Adds end-to-end speed metrics, per-model breakdowns for multi-model workflows, and tool-call duration tracking.
Industry Moves
Why it matters: Partnerships, funding, and platform shifts are shaping where models get adopted (education, enterprise, and developer ecosystems), not just who has the best benchmark score.
- Anthropic ↔ CodePath partnership: Anthropic is bringing Claude and Claude Code to 20,000+ students at community colleges, state schools, and HBCUs via CodePath.
- Anthropic board appointment: Chris Liddell was appointed to Anthropic’s Board of Directors (previously CFO of Microsoft and GM; also served as Deputy Chief of Staff during the first Trump administration).
- Anthropic funding and scaling plans: Anthropic said it raised $30B at a $380B post-money valuation, and stated the funding will deepen research, innovate in products, and expand infrastructure to make Claude broadly available.
- MiniMax distribution partnerships: Fireworks AI announced day‑0 launch partnership for MiniMax M2.5, positioning it for production agents.
Policy & Regulation
Why it matters: Competitive dynamics are increasingly spilling into policy (e.g., distillation/extraction concerns), while practical governance issues are emerging around misuse and privacy.
- OpenAI vs DeepSeek (policy warning): Bloomberg reports OpenAI warned U.S. lawmakers that DeepSeek is using “unfair and increasingly sophisticated methods” to extract results from leading U.S. AI models to train its next generation.
- Open-source jailbreak tooling debate: A discussion raises concerns about open-sourcing repositories that automate jailbreaking of open-weight models and potential misuse (e.g., weapon instructions, CSAM generation).
- Hiring/privacy concern: A purported interview practice of using AI to analyze private conversations was criticized as a “serious privacy violation,” with a note that anonymous viral claims may be unreliable.
Quick Takes
Why it matters: These are smaller items, but they often become default building blocks (datasets, eval harnesses, agent frameworks) or signal where the ecosystem is headed.
- IBM + Common Crawl: Common Crawl open-sourced annotations for IBM’s GneissWeb (a 10-trillion-token dataset) used for Granite’s core linguistic capabilities.
- Mistral Ministral 3 family: Released open-weights models (14B/8B/3B) compressed via “cascade distillation,” described as rivaling similarly sized competitors while using less training data/compute.
- Baseten replication of Generative Adversarial Distillation (GAD): Distilled Qwen3‑4B from GPT‑5.2; frames distillation as on-policy with an adaptive discriminator reward.
- DeepSpeed ZeRO load-time improvement: Tensor flattening reworked to happen on GPU (instead of CPU) to load huge multi-GPU models faster.
- Karpathy’s minimal GPT implementation: Training + inference GPT in 243 lines of dependency-free Python (core algorithm), with a separate visualization of the math-op DAG for a tiny forward pass.
Sahil Bloom
All-In Podcast
David Sacks
Most compelling recommendation: a check on “blind efficiency”
- Title: X article (title not provided) on the perils of blind efficiency (“optimize the life out of life”)
- Content type: Article (X article)
- Author/creator: Sahil Bloom
- Link/URL: http://x.com/i/article/2022361151342845952
- Recommended by: Scott Belsky
- Key takeaway (as shared): A warning about the perils of pursuing efficiency blindly—and a reminder not to “optimize the life out of life” by overlooking the hidden values in things tech increasingly does for us .
- Why it matters: It’s a clean decision-making lens for automation: efficiency gains can come with less-visible tradeoffs if the “stuff we outsource” also carried meaning, learning, or other latent value .
Related pointer: Sahil Bloom’s post linking to the same idea .
Also flagged: “Something Big Is Happening” (career opportunity for AI early adopters)
- Title: Something Big Is Happening
- Content type: Article
- Author/creator: Matt Schumer
- Link/URL: Not provided in the source clip
- Recommended by: David Sacks (referenced in conversation)
- Key takeaway (as shared): Sacks points to the piece as a viral article describing a career opportunity that will be available to “AI early adopters” .
- Why it matters: It’s a direct, high-level signal from an investor/operator that “AI early adoption” is being framed as a near-term career differentiator (at least enough to cite it as a notable viral argument) .
“And there was an article that went viral this week by Matt Schumer called Something Big Is Happening where he talked about this career opportunity that's going to be available to kind of AI early adopters.”
A useful tension across today’s two picks
Taken together, these recommendations form a practical counterbalance:
- One emphasizes opportunity for people who adopt AI early (as described in Schumer’s article, per Sacks) .
- The other emphasizes the risk of over-optimizing for efficiency, especially when tech removes activities with hidden value (as flagged by Belsky via Sahil Bloom) .
Product Growth
Product Management
Product Management
Big Ideas
1) PLG is an operating model—not a growth tactic
Product-led growth (PLG) is framed as organizing and building a company by putting the product first across acquisition, conversion, engagement, retention, and monetization .
What enables PLG to work, per the same framework:
- Strategy: CEO-level buy-in plus support from sales and marketing (called out as a common failure point) .
- Tactics: execution across the full stack of PLG levers .
- People: PMs who can drive discovery and a culture of rapid experimentation .
The 7-layer model used to structure PLG work:
- Go to market
- Information for decision
- Free-to-paid conversion
- Activation
- Retention
- Monetization
- Expansion
Why it matters: it gives PMs a shared map to diagnose where growth is actually breaking (vs. jumping to random experiments).
How to apply: start with the layer you’re “most broken” in, then address 1–2 layers below it (don’t optimize downstream levers if you lack volume upstream) .
2) Acquisition is shifting from traditional channels to product-led channels
Aakash Gupta explicitly calls out a shift from traditional marketing channels to product-led channels, and uses Canva as an example of focusing on product + user needs rather than naming competitors .
Why it matters: it changes what “good marketing” looks like for PLG—often closer to shipping product surfaces that acquire users than to classic brand spend.
How to apply: audit your top acquisition motions and ask: Is the product doing the acquiring, or are we relying on external campaigns to create demand?.
3) The “context trap”: context isn’t just transmitted—it’s produced through interaction
The Beautiful Mess argues that context is produced through interactions, not simply pooled or transmitted as background information . It contrasts a broadcast model of communication (message + background context) with approaches where alignment emerges via engagement with the situation .
"But context is produced through the interactions themselves."
Leadership implication: intent isn’t simply broadcast; it’s refined through dialogue, backbriefs, scenario exploration, and continuous adjustment—so intent becomes the context within which decisions are made . This is summarized as: “Context engineering is, in many settings, interaction design.”
Why it matters: PMs often default to “more docs / more pre-reads” as the fix for misalignment; this argues some alignment only emerges through the right interactions.
How to apply: treat key cross-functional decisions as an interaction design problem (not just a documentation problem), and choose the collaboration mechanism based on the type of context required .
4) Faster feedback loops as a moat—AI should automate the sorting, not the thinking
One PM community thesis: a defensible moat can come from a faster/better feedback loop (alongside proprietary data and distribution) . The suggested AI role is to connect into GTM systems and automatically extract and group customer questions, requests, and bugs —so PMs can spend time on user interviews and interpretation.
The same thread argues PMs should still own prioritization, interviews, PRDs, and acceptance criteria because a pipeline won’t know build effort, underlying motivations (“need behind the need”), or why customers want something .
Why it matters: it’s a concrete division of labor: use AI to remove manual tagging/search, but keep human judgment where it depends on real constraints and interpretation .
How to apply: automate intake + grouping, then explicitly reserve PM time for interviews and decision-making work that requires context and tradeoffs .
Tactical Playbook
1) Use the 7-layer PLG framework as a weekly diagnostic (not a one-time model)
Steps
- Name the broken layer (e.g., Go-to-market isn’t generating PLG leads; activation drop-off; poor conversion) .
- Work one layer up and 1–2 layers down from that failure point to find root causes and unblock testing volume .
- If layer 1 (Go-to-market) is the issue, focus on the two “you need”: marketing and website.
Why it matters: the framework warns against monetization experiments “6 layers later” when acquisition volume is the limiting factor .
2) A practical GTM checklist from the Slack vs. Canva contrast
If you’re trying to improve PLG acquisition, the examples provide a concrete way to benchmark your funnel entry.
A) Website patterns to evaluate
- Slack (2018): a “crazy simple” one-frame page with a clear positioning statement (“Where work happens”), an email CTA that uses a magic link (no password setup), client logos, and “Try it for free” emphasis .
- Canva (2026): a longer, more personalized homepage with positioning (“What will you design today?”), immediate persona-based journeys (workplace/educator/creator) mapping to plan choice, product showcase slots, template gallery, AI feature messaging, and “Sign up for free” CTA .
B) Marketing motion patterns to evaluate
- Slack (2018): traditional brand advertising (banner ads, buses, billboards, TV) positioning against “email” .
- Canva (2026): product-led SEO/AEO/GEO—creating high-volume pages that land users directly in a free tool (example: “build an Instagram post” → one click into a tool, no login, no credit card) .
How to apply: run a “first 60 seconds” audit:
- Can a new user reach value immediately (or are you gating with forms/passwords)?
- Do you personalize the journey fast enough for multiple user types to self-identify and choose a path?
- Do you have product surfaces that capture high-intent search demand and deliver value without friction?
3) Design decision-making interactions based on the type of context required
The Beautiful Mess highlights that not all decisions require the same kind of context:
- Some are rules-based (little situational awareness needed once rules are clear) .
- Some can be documented and supplied ahead of time.
- Some are emergent—context can’t be separated from the activity because it unfolds through coordination and action .
How to apply
- Classify the decision into one of the three context types above .
- If it’s emergent, don’t over-rely on “broadcast context.” Instead, use mechanisms like dialogue, backbriefs, scenario exploration, and continuous adjustment so alignment emerges through engagement .
- Treat the PM’s job here as “interaction design” (designing the process that produces shared context) .
Case Studies & Lessons
1) Slack’s 2018 PLG entry point: remove setup friction and get users into the product
Slack reportedly grew from 0 to 8 million daily active users in 4 years. The GTM mix highlighted includes traditional advertising positioning against email and a homepage designed to drop users into the product quickly via email + magic link (no password setup) .
Lesson for PMs: if activation depends on getting users into a workspace quickly, the website/onboarding experience is a product surface—not just marketing collateral.
What to copy: evaluate whether your initial CTA removes steps (like password creation) and gets users to an “aha” state with minimal delay .
2) Canva’s 2026 playbook: product-led acquisition plus user-centric messaging
Canva is cited at 260M monthly active users, $3.5B ARR, and 40%+ growth per year. The playbook emphasizes:
- Product-led SEO/AEO/GEO that routes high-intent searches into a free tool experience with no login and no credit card.
- A homepage that immediately personalizes by user type and showcases templates and AI capabilities, with “Sign up for free” as the CTA .
A specific positioning lesson: Canva is described as “stealing $2B ARR from Adobe,” but Adobe wasn’t mentioned on Canva’s website—“focus on the product and your user” .
Lesson for PMs: rather than leading with competitor comparisons, lead with a user need and a fast path to value.
What to copy: build acquisition surfaces that deliver value in one click, and align the homepage around user-specific paths (personas → plan fit) .
Career Corner
1) Competing with internal candidates for a platform PM role: de-risk ramp + show platform ownership
In a thread about interviewing against internal candidates for a role building an internal UI platform/framework across web, iOS, and Android, the core role requirement is interviewing developers and building for them—where engineering fluency and trust matter .
A key framing: internal vs external tradeoffs are often speed/context/relationships (internal) versus pattern recognition and technical credibility (external); the role described is said to lean toward the second bucket .
How to apply in interviews
-
Reduce perceived risk in two areas:
- Show you can ramp on internal context quickly (examples: map stakeholders, find early adopters, ship something useful before full alignment) .
- Demonstrate you think like a platform owner (adoption, trust, backwards compatibility, long-term maintenance) .
- With a Director of Engineering: emphasize partnership (translate developer pain into product decisions, handle pushback, balance consistency with team autonomy) to be seen as a peer—not a requirements collector .
- One commenter argues “internal context” is less important than product/tech knowledge plus a proven track record; the interview job is to convince a small set of people you could do the work quickly .
2) Big tech → startup: recognize the explicit tradeoffs, then validate the environment
A PM considering leaving big tech after a 4-year cliff cited a potential 25% compensation bump and more interesting product work, weighing it against WLB impact and risk .
Community responses emphasized:
- Assess work environment and founders’ experience; some startup environments can be “too difficult to work in” .
- Another commenter who quit big tech to start a company said it was “totally worth it,” and argued the equation is tilted toward startups “right now” (linked to their outlook on AI), with the comp bump making it sweeter .
How to apply: treat founder experience and day-to-day environment as first-order diligence inputs—not afterthoughts .
Tools & Resources
- PLG framework and examples (Aakash Gupta): “How to build product-led growth in 2026 (the complete 7-layer playbook)”https://www.news.aakashg.com/p/plg-in-2026
- Product-led SEO/AEO/GEO link referenced in the PLG post: https://www.news.aakashg.com/p/guide-aeo-geo
- The Beautiful Mess on the context trap: https://cutlefish.substack.com/p/tbm-406-seeing-everything-understanding
- Shannon–Weaver model (linked in the context piece): https://en.wikipedia.org/wiki/Shannon%E2%80%93Weaver_model
- Reddit: Platform PM interview thread (internal vs external): https://www.reddit.com/r/ProductManagement/comments/1r3vfpb/
- Reddit: Faster feedback loops + AI grouping idea: https://www.reddit.com/r/ProductManagement/comments/1r3ifeq/comment/o54l3pz/
- Reddit: Big tech → startup tradeoffs: https://www.reddit.com/r/ProductManagement/comments/1r4d4bs/
- Documentation pain point: a post notes documentation is still manual even with GPT and claims a free way to generate product guides with automatic screenshots and video demos “in like… 40 seconds” (via DM to the author)
Angie Setzer
Dept. of Agriculture
Successful Farming
Market Movers
Grains & oilseeds (U.S. + South America)
- Soybeans: March ’26 futures closed near $11.37/bu (+~13¢), with strength tied to reports the U.S.–China trade truce could be extended by up to a year and renewed talk of China lifting total U.S. soybean purchases to 20 MMT. At the same time, weekly export sales hit a marketing-year low at 11M bushels, down 36% WoW and 80% vs. the prior 4-week average .
- Corn: USDA raised U.S. corn exports to a record 3.3B bushels (+100M) and lowered ending stocks to ~2.1B bushels. Weekly net corn sales were reported at 81M bushels (up 99% WoW) and export pace was described as still ahead of the level needed to hit USDA’s higher projection .
- Wheat: USDA showed wheat feed/food use down 5M bushels, lifting stocks to 916M in one set of highlights . Weekly net wheat sales were 18M bushels (up 31% WoW), led by the Philippines.
Livestock (U.S.)
- Cattle: 5-market fed steer average was $241/cwt through Thursday (flat) with expectations it could finish higher . February live cattle futures ended the week at $242.93/cwt (up >$5 WoW) .
- Beef/pork values: Choice boxed beef was $364.47/cwt (down $4.23 WoW) and Select $363.42/cwt (down $0.41) . National base hog carcass price was $85.35/cwt (+$0.47) while pork cutout was $94.87/cwt (down $0.15) .
Trade & policy signals affecting demand
- U.S.–China (soy focus): Soybean prices were repeatedly tied to optimism that the trade truce may be extended and that it could support additional Chinese buying .
- U.S.–Taiwan deal: A finalized agreement was reported to cut tariffs and expand market access in Asia, including U.S. beef, dairy, pork, and wheat, alongside Taiwan’s pledge to buy >$44B in LNG and crude oil . Related coverage noted livestock/dairy/trade groups praising the agreement for expanding market access .
- Beef imports (U.S.): Following an executive order, the U.S. will temporarily expand beef imports from Argentina, raising the tariff-rate quota for lean beef trimmings by 80,000 MT for 2026, starting Feb. 13, with the stated goal of easing ground beef prices amid historically high costs and a shrinking U.S. cattle herd .
Innovation Spotlight
Vertical integration + AI in produce packing (U.S.)
Alsum Farms and Produce (Wisconsin) was highlighted for controlling growing, packing, washing, sorting, and distribution of millions of pounds of potatoes, pumpkins, and produce annually . The operation is using AI for sizing/sorting to improve speed and efficiency .
Special fertilizers and organo-mineral formulations (Brazil)
- Brazil’s special fertilizers segment grew about 19% in 2024, with expectations of continued growth through 2026 .
- Coverage emphasized these products are intended to complement, not replace, traditional fertilizers—aiming to improve nutrient-use efficiency and reduce losses .
- Organo-mineral production examples included converting leather-derived residues via hydrolysis into inputs providing amino acids and organic nitrogen within fertilizer formulations .
Precision livestock operations with measurable controls (Brazil)
A Mato Grosso do Sul swine operation expanded from 2017 into 3 nuclei / 12 barns, with licensed capacity up to 33,000 pigs. Tech and management elements highlighted included solar panels, biodigester infrastructure, and a robot to aid animal counting, plus weekly KPI monitoring for animal well-being, conversion, and mortality .
Regional Developments
U.S.: drought and early-season weather context
- The Corn Belt saw little to no rainfall over the last week in one update; drought conditions were largely unchanged overall but worsened in parts of Illinois, Iowa, Missouri, and across portions of Nebraska and Kansas.
- Reported drought exposure by area: corn country 31%, soybeans 37%, winter wheat 45%, spring wheat 11%, cattle country 39%.
- Another outlook described a warm stretch (highs in the 60s–70s) across the Plains/Midwest, while dry and drought conditions remained a concern for winter wheat heading toward spring .
Brazil: storms, harvest windows, and export constraints
- Severe storms were forecast across southern Brazil (RS/SC/PR), including hail, winds >100 km/h, and potential tornado/microburst conditions—posing risk to field operations and harvest activity .
- In central production areas, producers were urged to use a near-term window to harvest soybeans and finish planting second-crop corn and cotton before heavier rain (up to ~100 mm in 5 days) disrupts operations .
- Soy harvest progress: Conab cited ~17.4% harvested across the 12 main producing states, with Mato Grosso at 46.8%.
Brazil–China beef quota (Brazil)
China’s safeguard quota for Brazil was reported at ~1.06M tons, and January shipments were >119k tons (~11% of quota), reflecting an early-year rush by exporters/importers to secure quota share . At the current pace, quota exhaustion by Aug/Sep was flagged, after which exports would face an additional 55% tariff viewed as making exports to China largely unviable .
Best Practices
Weed control: focus on multiple modes of action + residual layering (U.S.)
- Corteva’s Eric Schurter emphasized building herbicide programs around multiple effective modes of action (at least two) and layering residuals to maintain control through canopy closure .
- For soybeans, he noted >65% of U.S. soybean acres are Enlist E3, and stressed pre-plant burndown / pre-emergence execution because many tools are pre-only and earlier planting extends the time residuals must last .
Bringing CRP/pasture back into crop production (U.S.)
Ag PhD’s guidance prioritized:
- Soil testing first, recommending grid sizes ≤5 acres to capture variability .
- Preserving built organic matter by avoiding unnecessary full-scale tillage, while still acknowledging some tillage may be needed due to rodents/erosion .
- Using Roundup-tolerant soybeans to control perennial grasses, or planning extra nitrogen if planting corn/wheat due to high-carbon residue .
Marketing posture noted by analysts (U.S.)
One set of producer guidance suggested rallies remain selling opportunities in an oversupplied global environment, including a recommendation to consider forward-selling 40–60% of the 2026 crop and “reward the rallies” .
Input Markets
Fertilizer: nitrogen tightness + high phosphate ratios (global)
- Urea values were reported climbing amid U.S.–Iran tensions, Europe running at ~75% of normal production, and uncertainty around China’s 2026 export plans .
- DAP in New Orleans was cited around $625, with December 2026 corn at $4.56 (ratio ~137) .
- Potash was described as the only major input still “well priced,” with manufacturers keeping values steady relative to grain .
Input-cost policy + insurance structure (U.S.)
A Farm Journal segment described crop insurance changes tied to the “One Big Beautiful Bill,” including higher premium subsidies at several coverage levels and a shift in how payment limits apply for LLCs/S corporations (now based on the number of equal owners) .
Forward Outlook
Key dates and planning items
- China trade timeline: Markets were described as tuned to President Trump’s scheduled April China visit , with sources emphasizing that a signed agreement would provide needed certainty for the market .
- USDA Outlook Forum: Outlook Forum figures were flagged as coming next week.
- USDA data confidence: A Farm Journal survey reported eroding confidence in USDA reporting—68% of economists, 73% of producers, and 78% of retailers said they’re less confident than in the past . NASS has launched an internal review after market-moving revisions to 2025 corn acreage estimates .
Farm financial backdrop (U.S.)
- USDA’s 2026 net farm income forecast was $153.4B (down 0.7% vs. 2025) and net cash farm income $158.5B (up 3%) . Direct government payments were forecast to rise ~$14B in 2026 while commodity cash receipts were forecast to fall ~$14B.
- Farm Bureau cited 315 Chapter 12 bankruptcies in 2025 (+46% vs. 2024) .
Active USDA programs (deadlines + totals)
- USDA announced $1B in assistance for specialty crop and sugar producers impacted by unfair market disruptions; reporting 2025 acres to FSA is due March 13, 2026. Details were linked in USDA’s release: https://www.usda.gov/about-usda/news/press-releases/2026/02/13/usda-announces-assistance-specialty-crop-farmers-impacted-unfair-market-disruptions.
- USDA issued final ELRP payments totaling >$1.89B (including 100% lump-sum payments for eligible flood/wildfire assistance applicants) .
- USDA also announced >$212M via the Market Access and Foreign Market Development programs to support export market development by farm groups .
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