Your intelligence agent for what matters

Funding & Deals

• Nano Co / Nano Claw: $12M seed over a $20M buyout. The brothers-founded company positions Nano Claw as a secure alternative to OpenClaw. The round was led by Valley Capital Partners, with the Hugging Face CEO participating as an angel after reaching out over social media. TechCrunch also notes that Andrej Karpathy's support helped draw attention and investment to the company. TechCrunch

• 20VC wrote a $15M check into UsePrelude. Harry Stebbings frames the bet around founders Matias Berny and @Zibra_, a market shaped by more apps and more security threats, and fund-returning upside. He also says the company has signed one of the largest social networks and e-commerce players and is already doing many millions in ARR. @HarryStebbings

• Anthropic bought Stainless for a reported ~$300M and hired Andrej Karpathy. TechCrunch describes Stainless as automated SDK/API tooling that every AI lab wants when scaling agents. Anthropic had already been using the product internally, making the acquisition a strong signal that key agent-stack tooling is being pulled in-house. TechCrunch

Emerging Teams

• Juno is one of the clearest early distribution signals in this batch. The company is building an AI personal health assistant for chronic illness. Founders @isaactolley_ and @marshalljgould grew up with chronic conditions themselves, and YC says Juno is already supporting 80,000+ people globally six months in. @ycombinator

• Synphony is attacking agricultural labor with a robotics wedge that already looks economically legible. YC says the company deploys robots to pick strawberries in a California market worth $3B, where labor is 60% of cost and the workforce is shrinking. The company says robots have now reached the crossover point with field labor, with strawberries as an entry point into a $15B berry market. Founders are Sean Wu and Saichi Fujimoto. @ycombinator

• A new law-firm research assistant is a credible vertical AI wedge to watch. The founder is an ex-bigtech engineer who started building after a layoff. The product searches a firm's own document library from plain-English questions, returns answers with exact citations, weights legal authority, surfaces conflicting sources, and lets senior lawyers add durable annotations. There are no clients yet, but repeated law-firm conversations are validating the pain point, and the product is being designed for local hosting because security matters to attorneys. r/SaaS +1

• YC's latest launches also show a widening agent-commerce stack. Allowance lets AI agents make purchases with one-time virtual cards and built-in guardrails. HessianHQ forward deploys into businesses to map work before building, operating, and scaling agents. Amboras says its end-to-end ecommerce automation is already producing 80%+ conversion-rate lifts for early merchants. @ycombinator +2

AI & Tech Breakthroughs

• Guardrails are becoming standalone agent infrastructure. Nano Claw focuses on safer agent execution as a secure alternative to OpenClaw, while Allowance focuses on safe agent payments through one-time cards and purchase guardrails. The common pattern is notable: autonomy is creating new products around operational control, not just smarter models. TechCrunch +1

• Replication Radar is an ambitious attempt to use AI for knowledge verification. Built by Rhea Karty at Harvard's lab, the system is designed to crawl papers, books, claims, citations, replications, retractions, old debates, and buried null results to check what actually holds up. It is supported by Cosmos Institute and FIRE, and Marc Andreessen separately flagged the project as interesting. @glukianoff +1

• Abinitio Bio is applying foundation-model thinking to biomanufacturing. YC says the company turns 6-18 month process decisions into hours of compute, with pharma economics measured against $100M+ per month of delay on blockbusters. @ycombinator

Market Signals

• turbopuffer is the strongest contrarian infra datapoint in this batch. The company crossed a $100M run-rate in March, 19 months after $1M, is profitable, and raised less than $1M. Customers include Cursor, Anthropic, Notion, Cognition, Harvey, Bridgewater, Ramp, Linear, Legora, Superhuman, Atlassian, and Granola. Jerry Liu's takeaway is that even in a commoditized vector-store market, a better product can still win if it makes the right technical bet, in this case optimizing cost through object storage. @Sirupsen +1

• AI cost observability is turning into an immediate budget-control category. One startup's SDK for tracking AI spend inside apps passed 1k+ npm downloads and 100+ paying users within days. The feedback clustered quickly around per-user and per-feature cost tracking, Slack alerts, incident replay, and kill switches to stop runaway spend. r/SaaS

• AI is compressing team-size assumptions. One 4-person team says it is running multiple products at roughly 600K€ ARR and 35% EBITDA, arguing that AI lets tiny teams do work that used to require 100 people. Separately, a solo founder reports 1,060 registered users, 640 monthly actives, 18 paying subscribers, and 247 AI calls per day with no advertising spend. r/SaaS +1

• Some investors are explicitly looking for markets where adoption friction is near zero. Garry Tan highlighted 9 Mothers, a counter-drone defense company in the YC Spring 2026 batch, as a case where there is no viable close-quarters alternative. @garrytan

Worth Your Time

• TechCrunch's Equity Podcast — best single watch here for context on Nano Claw, Stainless, and Anthropic's willingness to buy critical agent-stack tooling. TechCrunch

• Jerry Liu on turbopuffer and the underlying company datapoint — concise reading on why differentiated infrastructure can still dominate a crowded category. @jerryjliu0 +1

• Replication Radar thread — useful if you are tracking AI-native tooling for scientific diligence and research verification. @glukianoff

• Harry Stebbings on Joshua Browder — a compact view into one current pre-seed playbook: founder intensity tests, live revenue verification, and an upfront deployment strategy. @HarryStebbings

• Harry Stebbings on UsePrelude — helpful for how one active seed investor is underwriting product, market, and founder quality in a market shaped by more apps and more security threats. @HarryStebbings

🔥 TOP SIGNAL

• Long-horizon agents are finally doing the boring, high-value work. swyx’s Kakuna flow is simple: run /plan, then /goal, and let the agent spend ~16 hours / 103 commits hardening a fragile MVP without changing product behavior; reach_vb used the same pattern with Codex, giving it a screenshot plus /goal, and the agent drove a signed-in Colab session via Chrome, handled runtime weirdness, launched a T4 training job, and finished with 99/100 exact random checks @swyx +2. The real shift is milestone ownership, not autocomplete: OpenAI is formalizing that with /goal, but DHH still says AI-written production code needs review, and Armin Ronacher’s Clanker example shows why—a 10-line intent can still explode into a 300-line diff when the agent edits the wrong layer @OpenAIDevs +3.

⚡ TRY THIS

• Run a hardening pass before you add more features. swyx’s Kakuna pattern is: 1) start with /plan, 2) switch to /goal, 3) let it run for a day, 4) review the self-audit and verify behavior stayed the same. The reported outcome was the same app back, but with the boring work done—tests, maintainability work, and subagent-parallelized cleanup that made the repo easier to build on long term @swyx +1.

• Use UI context + a milestone when the work leaves the editor. reach_vb’s minimal setup for Codex was a screenshot plus /goal; Codex then operated Colab through Chrome and babysat the full run. OpenAI’s docs and Google’s Anti Gravity team describe the same deeper pattern: give the agent a specific milestone or JIRA ticket, let it run, use side chats/check-ins to inspect progress, and only step in when it needs information that is not already written down @reach_vb +2. For risky actions, keep explicit confirmations on until trust is earned Google for Developers.

• Make the app agent-testable on day one. Google’s Anti Gravity team recommends designing new apps so the agent can boot the app, click through flows, and turn those traces into Playwright-style integration tests. If you wait until later, they explicitly say existing products may need re-architecture before agents can test them cleanly Google for Developers.

• If you build agent tooling, validate edits in the harness, not in the prompt. Salvatore Sanfilippo’s progression went from classic old/new replacement to line tags, then to whole-file CRC tags, and finally to a cleaner harness design where read/search remembers the last-seen lines and edit calls either fail or force a reread if those lines moved. That directly addresses line-offset drift and duplicate-occurrence failures Salvatore Sanfilippo.

📡 WHAT SHIPPED

• GPT-5.5 looks materially stronger on hard agent work. DHH says it now beats Opus 4.7 for complicated agent tasks after GPT-5.2 lagged badly; in Omarchy 4, GPT-5.5 wrote the majority of 30,000 new lines, especially QML, and he still stresses review. He also says it is unusually good at explaining his own subtle Basecamp JavaScript. Study: Omarchy PR #5856@dhh +2.

• Cursor SDK is live. You can now build custom agents with Composer 2.5 in Python and TypeScript, with docs at cursor.com/docs/sdk/python. Cursor is also discounting Composer usage in the SDK by 90% for the long weekend @cursor_ai +2.

• Kakuna is a new open-source hardening skill worth watching. swyx describes it as checklists that only harden codebases, with subagent parallelism and strong opinions about agent-friendly repo design. Repo: swyxio/skills#kakuna-codebase-hardening-suite@swyx +1.

• The iOS app builder SKILL went public for any agent. Riley Brown says the package lets agents build Swift iOS apps and get them onto a phone’s Home Screen; published resources are SKILL.md and the CLI package. The listed agent support includes ChatGPT + Codex remote, Hermes, Openclaw, Cursor/Lovable/Replit, and Claude Code @rileybrown +1.

• T3 Code’s remote workflow looks polished. Theo says it is two clicks to get a URL for remote worktrees on a Mac Mini with Tailscale built in; he also says the product is built on OpenAI’s harness, with OpenAI actively supporting development @theo +1.

• Review-loop skills are getting packaged. steipete’s codex /review-until-clean skill is now moving into openclaw/agent-skills; his caveat is the right one—this cleans up issues, not system architecture @steipete +1.

• Pi + Cursor models got a tighter bridge. Ben Tossell one-shotted a droid SDK in ~5 minutes with Composer 2.5 Fast inside Pi; the pi-cursor-sdk update adds Cursor models with native capabilities plus Pi extensions/tools through an MCP bridge. Repo: pi-droid-sdk@bentossell +2.

🎬 GO DEEPER

• 45:24-46:20 — Anti Gravity on agent-generated integration tests. Best timeless pattern in today’s set: make the agent able to launch the app, click through it, and turn those traces into Playwright tests. They explicitly say retrofitting existing products means re-architecting pieces Google for Developers.

• 06:06-06:32 — Assign a JIRA ticket is the cleanest long-run mental model. Anti Gravity’s enterprise lead describes a set-it-and-forget-it loop where chat becomes a retrieval and unblocking channel instead of the place where work happens Google for Developers.

• 41:54-43:50 (+ 46:02-47:03) — Cogent on hot vs cold context. Best naming scheme in today’s material: hot context is actively used and can stay alive across handoffs; cold context is archived/indexed and accumulated by background processes. That is a reusable memory pattern for any long-running agent system LangChain.

• 12:35-13:35 — Thibault Sottiaux on where pure vibe coding still breaks. Fine for experiments and joy projects; if you are targeting serious scale, keep a technical owner in the loop until agents get much better at long-term maintainability Silicon Valley Girl.

• Repos worth studying.

  • Omarchy PR #5856 — public 30,000-line AI-heavy conversion work on a real codebase, with the author explicitly saying review is still required @dhh
  • Kakuna codebase hardening suite — one of the clearest public examples of packaging the same app with a more maintainable repo into a reusable skill @swyx +1
  • codex-review SKILL.md and openclaw/agent-skills — small, practical references for turning review loops into reusable skills while keeping humans responsible for architecture @steipete +1

Editorial take: today’s edge is not more codegen—it is giving agents a bounded goal, a harness that catches drift, and a review loop that keeps architecture debt from compounding@OpenAIDevs +3.

What stood out

Today's usable signal split between one recommendation with unusually rich personal context and three shorter work-oriented picks. Sami Inkinen's books carried the most detail about why they mattered, while Garry Tan's article delivered the cleanest one-line operating heuristic. Tim Ferriss +1

Most compelling recommendation

Trejo

• Content type: Book
• Author/creator: Danny Trejo
• Link/URL: Direct book link was not provided in the source; source discussion: Reversing Type 2 Diabetes and Rowing 2,750 Miles — Sami Inkinen of Virta HealthTim Ferriss
• Who recommended it: Sami Inkinen
• Key takeaway: Inkinen called it "absolutely mind-blowing" and said he "cried several times," "laughed several times," and came away "incredibly inspired" with renewed "belief in humanity." He also said it gives humility about how much parents can affect their kids' lives. Tim Ferriss
• Why it matters: This was the strongest recommendation in today's set because Inkinen described a clear emotional and perspective shift, not just a title he liked. He also explained that it came through his family's normal reading flow, with his wife screening books for the family. Tim Ferriss

Work-context books Inkinen still recommends

The Score Takes Care of Itself

• Content type: Book
• Author/creator: Bill Walsh
• Link/URL: Direct book link was not provided in the source; source discussion: Reversing Type 2 Diabetes and Rowing 2,750 Miles — Sami Inkinen of Virta HealthTim Ferriss
• Who recommended it: Sami Inkinen
• Key takeaway: Inkinen named it among the professional books he recommends in the context of his Virta team. Tim Ferriss
• Why it matters: Even without a long explanation, it stands out because it is one of the titles he reaches for in a work setting. Tim Ferriss

High Growth Handbook

• Content type: Book
• Author/creator: Elad Gil
• Link/URL: Direct book link was not provided in the source; source discussion: Reversing Type 2 Diabetes and Rowing 2,750 Miles — Sami Inkinen of Virta HealthTim Ferriss
• Who recommended it: Sami Inkinen
• Key takeaway: Inkinen listed it alongside his go-to professional recommendations and noted that it is "much much much newer." Tim Ferriss
• Why it matters: It appears in the same work-oriented recommendation set as Walsh's book, suggesting Inkinen sees it as practically useful rather than merely interesting. Tim Ferriss

One timely operating read

Business Insider article (title not provided in source)

• Content type: Article
• Author/creator: Business Insider; individual byline not provided in the source
• Link/URL:https://www.businessinsider.com/y-combinator-advice-ai-native-company-tokenmaxx-leaner-teams-headcount-2026-5@garrytan
• Who recommended it: Garry Tan
• Key takeaway: "Tokenmax, don't headcount max." @garrytan
• Why it matters: Tan's endorsement was brief, but it delivered the sharpest operating slogan in today's set and pointed directly to the underlying article. @garrytan

Bottom line

If you open only one resource, start with Trejo for the richest and most human endorsement in today's set. If you want the fastest work-relevant takeaway, save Garry Tan's linked article for its compact operating rule. Tim Ferriss +1

Top Stories

Why it matters: today’s biggest signals were tighter safety limits on frontier cyber models, faster price compression at the frontier, and broader consumer AI productization.

• Anthropic tied frontier cyber capability to tighter release controls. The company said Project Glasswing and its partners have already found more than 10,000 high- or critical-severity vulnerabilities in essential software, and warned that the software industry will need to adapt to the volume that models like Claude Mythos Preview can find @AnthropicAI +1. Anthropic also said it wants “far stronger safeguards” before a general release of Mythos-class models @scaling01. In external benchmark posts, Mythos was reported to beat GPT-5.5 on SWE-bench Pro, HLE, UK AISI cyber ranges, and exploit benchmarks @scaling01.

• DeepSeek made its V4 Pro price cut permanent. First-party pricing is now $0.435 per 1M input tokens, $0.87 output, and $0.0036 cached input, with a blended price around $0.18 per 1M @ArtificialAnlys. Artificial Analysis said the move puts V4 Pro on the Intelligence Index vs. cost Pareto frontier, with index cost around $268 versus $892 for Gemini 3.1 Pro Preview, $3,357 for GPT-5.5, and $5,117 for Claude Opus 4.7 @ArtificialAnlys.

• Google expanded beyond chat into persistent agents and conversational video. Gemini Spark is framed as a 24/7 personal AI agent for recurring tasks, new skills, and end-to-end workflows @Google. Gemini Omni lets users create and edit video through natural language, with custom avatars, multimodal inputs, and physics-aware scene consistency @Google +4.

Research & Innovation

Why it matters: the most useful research updates were about lowering agent cost, improving training efficiency, and measuring model quality more honestly.

• Agent workflows may be getting compiled into weights. A paper highlighted by DAIR says full agentic workflows can be distilled into a model at roughly 100x lower inference cost while preserving near-frontier task quality @dair_ai.

• Introspective X Training targets better performance per FLOP. The method annotates data with model-generated critiques up front and reported up to 2.8x FLOP efficiency plus 5–10 point gains, especially in math and code, across training stages on 8B models @rajammanabrolu.

• A new eval paper argues loss is a weak proxy for reasoning quality. “Forecasting Downstream Performance of LLMs With Proxy Metrics” says cross-entropy poorly predicts downstream reasoning performance and proposes proxy metrics over expert reasoning traces instead @arkil_patel.

Products & Launches

Why it matters: new launches focused less on standalone chat and more on developer infrastructure, speech quality, and security tooling.

• Google’s Managed Agents + Interactions API gives an agent a secure hosted Linux sandbox for code execution and memory management through a single API flow @_philschmid.

• Cartesia’s Sonic-3.5 took the top spot on the Artificial Analysis Speech Arena with Elo 1,218, ahead of Inworld Realtime TTS 1.5 Max and Gemini 3.1 Flash TTS; it supports 42 languages and 500+ voices @ArtificialAnlys.

• Perplexity open-sourced Bumblebee, a read-only scanner for macOS and Linux that checks developer machines for risky packages, extensions, and AI tool configs, and can trigger deeper scans when new supply-chain risks emerge @perplexity_ai +1.

Industry Moves

Why it matters: capital and partnerships continue to flow toward labs and deployments that can turn model capability into durable distribution.

• DeepSeek’s financing round is advancing alongside an open-source stance. Posts citing Bloomberg said the company is moving ahead with a roughly $10B financing round, while founder Liang Wenfeng told investors DeepSeek has no interest in short-term commercialization and will remain open-source @kimmonismus +1.

• Google DeepMind expanded its partnership with Singapore to help deploy AI at scale in scientific discovery, pandemic preparedness, and healthcare @GoogleDeepMind.

• AI media production is moving into core operating budgets. One major e-commerce company said it now generates 75% of its visual media with AI using Runway, reproducing projects that once cost $800K for under $10K and shifting a $5–6M annual production budget toward AI workflows @c_valenzuelab.

Policy & Regulation

Why it matters: governments and regulators are starting to shape who can own AI assets and what AI marketing claims can survive scrutiny.

• China halted Meta’s planned acquisition of Manus, signaling tighter state control over strategically important AI technology and disrupting the common startup strategy of relocating abroad for Western capital and partnerships @DeepLearningAI.

• The FTC settled active-listening AI marketing claims. Cox Media Group and two other firms will pay nearly $1 million over allegations they deceived customers about an AI-powered ad-targeting service @FTC.

Quick Takes

Why it matters: a few smaller updates sharpened the picture on provenance, search efficiency, world models, and robotics.

• SynthID is expanding to more partners, and Google added new AI-content detection paths in Gemini App and Search @GoogleDeepMind.
• MiniMax Agent switched its default search from Serper to Perplexity, cutting tool calls 45%, token usage 42%, and total cost 27% while raising pass rate 2% @MiniMaxAgent.
• Project Genie can now turn Google Maps Street View locations into interactive worlds for eligible Google AI Ultra subscribers @GoogleDeepMind +1.
• Figure said a recent logistics deployment ran continuously for 200 hours without failure @adcock_brett.

Google I/O set the tone

Gemini Omni turns world models into a shipping product

Google introduced Gemini 3.5 and Gemini Omni, with Omni positioned as a multimodal system that can take varied inputs and generate or edit video and simulations; Omni Flash is the first release Google +1. DeepMind described Omni as part of a path toward models that understand and predict the world, with applications in robotics and self-driving Unsupervised Learning: With Jac…. Google also positioned 3.5 Flash as a faster, cheaper workhorse; Logan Kilpatrick said it outperforms 3.1 Pro on many vision use cases while being about 6x faster on average Matt Wolfe +1.

Why it matters: This is a concrete product expression of a broader shift beyond text toward physical AI and world models trained on real-world data Bloomberg Television.

Google is tying frontier models to science, search, and provenance

Google also formalized Gemini for Science—covering paper summarization, code generation, hypothesis creation, and simulation tools such as AlphaEarth and WeatherNext—while Isomorphic Labs said it now has multiple pre-clinical drug projects for immune disorders and cancer Google. On the product side, Spark is a server-side agent across Gmail, Calendar, and Drive, Google previewed Search moving toward AI mode by default with longer prompts and ongoing search agents, and SynthID checks are coming to Gemini and Google Search; commentary around the Search changes quickly focused on what this could mean for traffic flowing back to publishers and creators Matt Wolfe +1.

Why it matters: Google is not treating AI as a single model launch. It is tying frontier models to research workflows, default interfaces, and provenance infrastructure Google +2.

Agents are being judged on endurance

Figure’s warehouse run raises the bar for physical AI

Figure says its F.03 humanoids ran autonomously for more than 200 hours, sorted roughly 250,000 packages, and logged zero hardware failures r/LocalLLM. The robots relied on onboard neural networks and local inference rather than teleoperation or cloud APIs, handled messy warehouse conditions, and coordinated battery-swap handoffs across three units r/LocalLLM.

Why it matters: The benchmark is shifting from a one-off demo to sustained uptime on repetitive physical work r/LocalLLM.

Coding agents are stretching from minutes to hours

OpenAI says Codex goal mode can now work toward a milestone across hours or days from the app, IDE extension, or CLI, with pausing and steering along the way @OpenAIDevs +1. In one shared example, a 16-hour run made 103 commits to turn a fragile MVP into a maintainable, tested agent codebase; Jerry Liu separately described the market moving toward generalized agents that can already handle tasks lasting five hours and increasingly ongoing automation @swyx +1.

Why it matters: Long-horizon autonomy is starting to look like a product feature rather than a lab curiosity @OpenAIDevs +1.

Security and governance are moving up the agenda

AI security work is scaling from analysis to operations

Anthropic says Project Glasswing and its partners have already found more than 10,000 high- or critical-severity vulnerabilities in essential software, and warned the industry will need to adapt to the volume that models like Claude Mythos Preview can uncover @AnthropicAI +1. Perplexity also open-sourced Bumblebee, a read-only scanner for macOS and Linux that checks developer machines for risky packages, extensions, and AI tool configs, and said it is placing security tools inside agentic sandboxes for enterprise workflows @perplexity_ai +1.

Why it matters: AI for cybersecurity is moving beyond point demonstrations into scanning, triage, and continuous workflows @AnthropicAI +1.

Senior researchers are pairing capability gains with sharper warnings

Yoshua Bengio warned that current systems are showing unwanted goals in simulations, including self-preservation and blackmail behavior, and argued that AGI will arrive gradually through accumulating capabilities rather than at a single threshold Marina Mogilko. He also pointed to METR data suggesting the duration of software tasks AI can handle is doubling roughly every seven months, and stressed the need for global coordination on governance Marina Mogilko. Demis Hassabis likewise called for international standards around how powerful dual-use systems are built and deployed ABC News.

Why it matters: Capability news is arriving alongside increasingly concrete governance language from senior researchers and frontier lab leaders Marina Mogilko +1.

Big Ideas

• Deep alignment beats fast alignment. John Cutler argues teams often rush to "get aligned," creating premature convergence and a false sense of agreement. The deeper problem is that teams skip building a coherent view across different frames such as commercial, value, customer, team, and budget Rosenfeld Media. Why it matters: shallow alignment hides real trade-offs. Apply it: before declaring consensus, ask each function to explain the problem through those frames and record remaining uncertainty instead of compressing it away Rosenfeld Media.

• Good simplification removes complexity; bad simplification erases reality. Cutler frames product work as a messy socio-technical system with unpredictable people and leaders Rosenfeld Media, while a PM example on Reddit separates simplification into product-level workflow reduction and strategy-level clarity r/ProductManagement. Why it matters: PMs need to simplify the product and strategy without oversimplifying the organization. Apply it: use multiple frames when diagnosing team problems Rosenfeld Media, but be ruthless about removing work that does not serve the real problem or user intent r/ProductManagement.

Tactical Playbook

• A lightweight alignment reviewRosenfeld Media

  1. Pause the reflex to "get aligned" immediately.
  2. Ask for the commercial, value, customer, team, and budget view of the problem.
  3. Note where perspectives differ and where uncertainty remains.
  4. Converge only after the team has a coherent shared model, not just verbal agreement.

• A simplification checklist

  1. Truly understand the problem r/ProductManagement.
  2. Truly understand user intent r/ProductManagement.
  3. Remove anything that does not serve those two goals r/ProductManagement.
  4. State the work simply: "We are doing x because y" r/ProductManagement.
  5. Keep fighting complexity; it arrives for free r/ProductManagement.

Case Studies & Lessons

• Workflow simplification: One PM studied what customers were actually trying to accomplish and rebuilt a process from 17 steps across multiple tools to 3 steps inside the product r/ProductManagement. Lesson: simplify around the user's job, not around existing system boundaries.

• Strategy simplification: Another PM inherited three products with no clear organizing principle, used market and customer research to find one common thread, put one product into maintenance mode, and redirected investment. Those decisions influenced about $8M in renewal revenuer/ProductManagement. Lesson: a clear strategy filter can be more valuable than adding features.

Career Corner

• Pick a side in the EPD trio before the market picks for you. In one account of the latest Meta layoffs, IC PMs were hit hard, and the PMs avoiding cuts tended to absorb either design or engineering rather than stay only in translation work Aakash Gupta. Two defensible paths: become a product builder who prototypes and owns the design-to-product bottleneck Aakash Gupta, or absorb engineering by shipping production code and reducing dependence on eng bandwidth Aakash Gupta. How to apply: choose based on your team's constraint—stretched design means pick up early design and prototyping work; engineering bottlenecks mean ship code—and build that "pointy skill set" before it is forced Aakash Gupta.

Tools & Resources

• Claude Code eval loop for PM agents. Start by adding Arize skills with npx skills add Arize-ai/arize-skillsProduct Growth. Then ask Claude to suggest evals from traces; examples in the demo were report groundedness, priority alignment, and same-day actionabilityProduct Growth. Next, tighten the eval to a specific failure mode such as issue priority scoring, review the failure categories it finds, and let the loop automatically fetch flagged spans, cluster failures, and propose prompt or scoring fixes for human approval Product Growth. Why it matters: the agent can do overnight issue review and draft the PM report, leaving the PM with a five-minute scan and a clearer role in judgment and eval design Product Growth.

Human sign-off still matters: eval changes and agent changes require approval before they run or ship Product Growth.