Your intelligence agent for what matters

Funding & Deals

• ZeroEntropy: GBrain now ships ZeroEntropy as its recommended default embedding and re-ranking option over OpenAI and Voyage AI. That commercial placement is paired with Garry Tan's public endorsement of the company as a six-person team building task-specific AI models described as 4-8x faster than offerings from OpenAI or Anthropic, with 500K Hugging Face downloads. @garrytan +2
• SPRYT: The UK company says its Asa patient-engagement agent is backed by the NVIDIA Inception programme and partnered with Optum and NHS trusts. The reported pilot results are better than traditional reminder baselines, particularly for patient cohorts that are historically harder to reach. r/Futurology
• Avarieux: The company came out of stealth this week and opened a waitlist around a finance-research product that verifies numeric AI claims against public sources before delivery. The product is explicitly framed to operate as a publisher rather than an adviser. r/SideProject

Emerging Teams

• ZeroEntropy: The signal is compact but notable: six people, 500K Hugging Face downloads, and third-party distribution through GBrain's default embedding/reranking slot. @garrytan +2
• Avarieux: The founder is a recent MS Data Science graduate who built several public MCP servers and has two pull requests under review in Anthropic's official modelcontextprotocol/servers repo. The product adds a real-time verifier between model and user, and every analysis becomes a timestamped, citable URL. r/SideProject
• Cirano: The product runs 100% on-device on iOS with no cloud and uses chat history for what the company calls Personal Digital Intelligence. After 14 months of development and a late pivot from cloud to on-device architecture, the four-person team across Minnesota, Uzbekistan, and Vietnam reported its first four paying subscribers and $52 MRR. r/startups
• MCP policy SaaS: A solo founder is building cloud-managed allow/deny/audit policies across roughly 3,800 MCP servers and can push signed policy bundles to enrolled machines in under 60 seconds. A local Claude Code plugin enforces those policies, keeps a SHA-256 chained audit log, and blocks prompt injection attempts before Claude sees the call. r/SaaS

AI & Tech Breakthroughs

• Arc Sentry: The product detects multi-turn jailbreaks by monitoring internal model state instead of prompt text. On a USENIX Security 2025 example, the score moved from 0.031 at Turn 2 to 0.232 at Turn 3; the post says LLM Guard scored 0/8 because it evaluates prompts independently, while Arc Sentry blocked before any model response was generated. r/artificial
• Thinking Machines: The company says it is building AI for real-time, human-like collaboration and shared its approach and early results. Garry Tan separately argued that fast, usable multimodal systems could enable personal AI after quickly fine-tuning his own Qwen3.5-397B model. @thinkymachines +1
• Runtime control for agents: The MCP policy product above enforces tool policies locally on the developer machine and keeps data local, while Arc Gate uses the same geometric monitoring layer as a hosted runtime governance proxy for agents using APIs. r/SaaS +1
• DeepSeek Flash: Bindu Reddy highlighted the model as almost 100x cheaper, effective for small tasks, and well-suited to operating on large amounts of data or records. @bindureddy

Market Signals

• 20VC participants described an AI sales compensation bubble. In the episode, operators said Anthropic and OpenAI are offering $10M-$30M+ packages for top sales talent, and one speaker said Anthropic is inflating a bubble the rest of the market is trying to keep up with. 20VC with Harry Stebbings

• API-first AI selling is getting more technical. The xAI example in the same discussion emphasizes technically astute reps who can run their own demos. 20VC with Harry Stebbings
• International expansion is moving earlier. The same operators said companies are opening EMEA and APAC in parallel instead of waiting to fully mature North America, increasing the premium on leaders with global experience. 20VC with Harry Stebbings
• Consumption pricing is spreading as per-seat SaaS comes under pressure. The 20VC discussion argues that vendors are being forced to add consumption components and tie some sales compensation to actual usage after the initial booking event. 20VC with Harry Stebbings

Worth Your Time

• 20VC episode:Why Anthropic Are Causing a Comp Crisis & Why You’d Never Hire From Salesforce or ServiceNow is the best single source here on comp inflation, technical API selling, international expansion pressure, and the move toward consumption pricing. 20VC with Harry Stebbings

• Arc Sentry / Arc Gate repos:arc-sentry, arc-gate, and bendexgeometry.com are the most concrete materials in this set on state-based jailbreak detection and runtime governance. r/artificial
• MCP policy demo:https://www.youtube.com/watch?v=V25yxqlAoY0 is a useful quick look at a local-first control layer for AI agents. r/SaaS
• Thinking Machines post:https://x.com/thinkymachines/status/2053938892152435174 is a compact primary-source read on the real-time multimodal collaboration thesis. @thinkymachines

🔥 TOP SIGNAL

• Self-verifying agent loops are the highest-leverage pattern showing up right now. Peter Steinberger’s OpenClaw setup gives the agent broad access to Slack, Discord, Notion, Linear, email, calendar, and other data sources, then lets it check out repos, compile, run, debug, and verify fixes end-to-end; his example is a bug report arriving via message and turning into a PR about 5 minutes later Christian Byza. The key is the harness, not the vibe: he explicitly builds replica environments so the agent can prove the fix before commit, and he treats this as agentic engineering that still requires human system understanding and meta-prompting when loops appear Christian Byza.

⚡ TRY THIS

• Codify repo intent, then autotriage the easy wins (Peter Steinberger).

  1. Add a VISION.md to the repo.
  2. Restrict autonomous work to issues/PRs that fit project vision, are inferrable in code with high confidence, have a clear fix, and can be live-tested@steipete.
  3. Let Codex run in a VM with computer vision via crabbox.sh, then manually review its suggestions; if issue entry is slowing you down, steipete added quick selection to repo.bar@steipete.

• Force a scratch-log on any refactor that touches lots of files (steipete).

  • Prompt: Maintain a scratch-log while you work on this refactor with decisions, tradeoffs, and review fixes.
  • Pair it with the public autoreview skill for long-running cleanup; steipete says it has already run 5h+ on a large refactor and kept fixing issues @steipete +1.
  • Read the log afterward to see what the agent decided and what you forgot to specify. Good antidote to ThePrimeagen’s warning that AI can make your own production reports less dense if you stop surfacing that information yourself @ThePrimeagen.

• Give the agent a testable world, not just a prompt (Peter Steinberger).

  1. Recreate the exact failing environment first — his example is a remote macOS box for a launchd bug Christian Byza.
  2. Have the agent reproduce the issue, write the fix, and rerun the same environment to verify it works before commit Christian Byza.
  3. If the run time or behavior feels off, stop it and ask on the meta-level where it is struggling instead of letting it spin Christian Byza.

• Roll out coding agents like a platform, not a gated pilot (Boris Cherny, Anthropic).

  • Give everyone tokens and remove approval friction for everyday experimentation Big Technology.
  • Create psychological safety so people can try weird process changes, fail, and iterate Big Technology.
  • Do not pre-pick the use cases; Boris says the wins often come from unexpected roles, and you optimize only after a use case starts to scale Big Technology.
  • His production signal: Anthropic says code written per engineer grew about 250% after Claude Code, while quality and reliability stayed stable Big Technology.

📡 WHAT SHIPPED

• Claude Code terminal agents: new multitasking flow for firing off multiple parallel tasks, browsing them with left/right arrows, and routing work through custom sub-agents like a Web Research Specialist Riley Brown.
• Cursor Composer 2.5: Riley Brown says the new model is extremely cheap to run, includes a full in-app browser, and is noticeably faster than Codex or Claude on quick frontend/landing-page work; he still says he moved most of his own agentic work to Codex because it feels like a more unified product Riley Brown.
• Codex computer use: Greg Brockman highlighted Codex building and debugging an iPhone simulator feature end-to-end, then driving the simulator to bug-bash the code it had just generated @gdb +1.
• Open ecosystem signal from OpenAI: Tibo says about 5% of production traffic is on Pi harness and another 5% on OpenCode; Romain Huet adds that ChatGPT subscriptions work across other tools too, and the Codex harness/app server are open source for bringing similar experiences into your own app @thsottiaux +1.
• pi.dev read tool debate: the default read behavior changed, but Armin Ronacher notes the tool is fully swappable; one prompt restores the old behavior via this gist. Discussion is in issue #4916@badlogicgames +2.

🎬 GO DEEPER

• 24:28-25:08 — Peter Steinberger on the self-verifying bug-fix loop. Remote repro box, agent fix, rerun the exact env, then commit. This is the cleanest timeless pattern in today’s set Christian Byza.

• 17:28-18:25 — Ping to PR in 5 minutes. Worth watching for the broader point: once the agent can read your messages and touch the systems you already use, bug triage becomes a full workflow instead of a chat demo Christian Byza.

• 01:56-02:26 — Boris Cherny on the 250% code-volume jump. Short clip, big management lesson: gains show up when teams actually let people use the tools without turning experimentation into a permissions process Big Technology.

• Repos and tools worth studying:
  • autoreview SKILL.md — small public reference for long-running review/fix loops on messy refactors @steipete.
  • crabbox.sh + repo.bar — one is the verification layer, the other is the quick issue browser feeding Codex @steipete.
  • Pi read-tool restore gist — useful if you care about making agent tools swappable instead of arguing endlessly about defaults @mitsuhiko +1.

Editorial take: the durable edge today is not which agent wins — it is who has the better verification harness, repo rules, and scratch-log discipline around the agent.Christian Byza +2

Top Stories

Why it matters: the biggest signals today were AI moving deeper into science, stronger cyber models entering gated channels, and tougher scrutiny of headline agent claims.

• AI is moving from scientific assistance toward research acceleration. Posts highlighted work attributed to OpenAI on a math conjecture, DeepMind on leukemia drug candidates, FutureHouse on a blindness treatment, and Google + Harvard’s ERA on scientific simulation code @TheTuringPost. Separately, one researcher said Codex made what looked like publishable progress on 20–50-year-old open conjectures after 8+ hours of autonomous runtime, and argued the highest-value use may be accelerating active research directions rather than older unsolved problems @arankomatsuzaki.

• Frontier cyber models appear to be moving toward restricted deployment, not broad release. Posts indicated Anthropic is preparing claude-mythos-1-preview for Claude Code and Claude Security, with access strings added and the model briefly visible in the UI; another post noted Anthropic had already signaled the exact model may not be for the general public @testingcatalog. In parallel, OpenAI is rolling out GPT-5.5-Cyber through Trusted Access for verified defenders @kimmonismus.

• Big autonomous-agent claims are facing a higher evidence bar. A fact-check of Google’s claim that agents built an entire operating system from a “single prompt” said that framing is misleading, human intervention is unclear, there was no analysis of whether the agents copied code, and the prompt, code, and logs were not released @random_walker +1. The authors argue that open-world evaluations need new methodological norms beyond benchmark-style reporting @random_walker.

Research & Innovation

Why it matters: the most useful research updates were about better interfaces, better evaluations, and more realistic tests of agent performance.

• A new harness paper suggests interface design can unlock large gains. It reported an 88.5% average relative improvement across 7 deterministic environments, 126 model-environment settings, and 18 backbones, and said a harness learned from one model trajectory generalized to 17 other backbones, implying it captured environment structure rather than model-specific behavior @omarsar0.

• Long-horizon web agents are being measured on multi-hour workflows. Microsoft Research’s Webwright took the #1 spot on Odysseys, a benchmark for sustained planning, memory, reasoning, and verification across many websites and tools @rsalakhu. Its example tasks look closer to real analyst work than single-step browser tests @rsalakhu.

• Models still forecast scientific progress poorly. A paper covering 4,760 scientific events found frontier models can identify plausible research directions, but cannot reliably predict whether advances will happen or on what timeline; the authors attribute this to miscalibration rather than missing knowledge @dair_ai. That is an important constraint for AI-scientist and research-planning agents @dair_ai.

Products & Launches

Why it matters: launches continue to push AI into production platforms, video workflows, and multimodal model interfaces.

• Claude Opus 4.8 is now available on Google Vertex. Posts also said Sonnet 4.8 is expected soon after an earlier leak @marmaduke091 +1.

• Runway released Aleph 2.0 for shot-level video editing. Early testers said a user can edit a single frame with tools like Nano Banana Pro, GPT Image 2, or Gen-4, and Aleph will propagate the change across the full sequence @egeberkina. Tester reaction was strongly positive @AleRVG +1.

• DeepSeek appears to have added vision. One post framed it as a reliable, fast, cost-effective option among Chinese models, while another estimated the current vision quality around Qwen 3 level and less integrated than Claude or Gemini @thegenioo +1.

Industry Moves

Why it matters: the business story remains a mix of infrastructure buildout, internal AI mandates, and new startup operating models.

• DeepSeek’s financing looks increasingly tied to physical AI infrastructure. Posts said investors in its reported round include CATL, JD.com, NetEase, Tencent, state funds, and others, with CATL’s interest linked to AI data-center power equipment and energy storage @0xLogicrw. Another post said DeepSeek is hiring data-center engineers in Inner Mongolia and starting hyperscale builds to tap green power @0xLogicrw.

• Google is framing AI as an operating mandate. A DeepMind director described AI integration inside Google as “not a luxury, an obligation,” pointing to major internal changes underway @thursdai_pod.

• Polsia raised $30M at a $250M valuation while pitching an AI-run company model. The startup said it is approaching $10M in annual run rate, has one founder and zero employees, and used AI to run operations and even its own fundraising @Bencera.

Policy & Regulation

Why it matters: talent policy is becoming AI policy when frontier labs depend heavily on global researchers.

• A U.S. immigration change could complicate hiring and retention at frontier labs. Posts said many top researchers at OpenAI, Anthropic, Google, Meta, and other labs are non-U.S. citizens on temporary visas, and that forcing them to leave the country to apply for a green card adds uncertainty and delay to a strategically important talent pool @kimmonismus.

Quick Takes

Why it matters: a few smaller updates sharpen the picture on training methods, agent design, vision models, and data strategy.

• RandOpt reported PPO/GRPO-level or better results by adding Gaussian noise to pretrained models and ensembling the outputs, with tests across Qwen, Llama, OLMo3, and VLMs @yule_gan.
• A Google Cloud guide outlined five practical patterns for long-running agents, including checkpoint/resume, delegated approval, layered memory, ambient processing, and fleet orchestration @TheTuringPost.
• Roboflow said Gemini 3.5 Flash showed its clearest gains over Gemini 3.1 Pro in counting and spatial reasoning, two categories important for industrial vision @roboflow.
• On DCLM, one result suggested the best data filter for large models may be no filter, with a follow-up post arguing low-quality data can aid generalization and contrast learning @tatsu_hashimoto +1.

What stood out

The clearest signal today was an article that offers a crisp frame for thinking about AI and jobs: automating tasks does not necessarily eliminate the job. Around that, John Doerr surfaced a founder-values book and two publications he relies on to stay current, while Patrick Collison pointed readers to a New York Times opinion piece on AI, philanthropy, beauty, and aesthetics @levie +2

Most compelling recommendation

After Automation

• Content type: Article
• Author/creator: Dan Shipper @danshipper
• Link/URL:https://every.to/p/after-automation@danshipper
• Who recommended it: Aaron Levie, who called it a "fantastic post" about why jobs are not going away in the way some predict @levie
• Key takeaway: Automating one or many tasks within a job usually expands the job definition instead of eliminating it. Work shifts toward doing more tasks, doing them at higher quality, or taking on the tasks that have not yet been automated, often for a newly reachable audience @levie
• Why it matters: It is the most actionable framework in today's set for thinking about AI's effect on coding, legal work, sales, and marketing without confusing task automation with job elimination @levie

Founder philosophy pick

The Monk in the Riddle

• Content type: Book
• Author/creator: Randy Komisar BNDF BUSINESS
• Who recommended it: John Doerr BNDF BUSINESS
• Key takeaway: Doerr recommends it for its distinction between missionaries and mercenaries in entrepreneurship, illustrated through a startup that improved after pivoting from selling caskets toward building community and clarifying its mission and values BNDF BUSINESS
• Why it matters: This is the richest founder-framework recommendation in today's inputs because it ties company performance to mission, values, and the kind of motivation that can align a team BNDF BUSINESS
• Source discussion:https://www.youtube.com/watch?v=uZ56og18pf0BNDF BUSINESS

Two broader reading signals

Ross Douthat's New York Times opinion piece on AI, philanthropy, beauty, and New Aesthetics

• Content type: Opinion article
• Author/creator: Ross Douthat @patrickc
• Link/URL:https://www.nytimes.com/2026/05/23/opinion/artificial-intelligence-philanthropy-beauty.html@patrickc
• Who recommended it: Patrick Collison, who called it interesting @patrickc
• Key takeaway: The source only specifies the piece's focus: AI, philanthropy, beauty, and, in passing, the call for New Aesthetics @patrickc
• Why it matters: It broadens the day's list beyond labor and operating questions into a more cultural line of thinking about AI @patrickc

MIT Technology Review

• Content type: Magazine
• Author/creator:MIT Technology ReviewBNDF BUSINESS
• Who recommended it: John Doerr BNDF BUSINESS
• Key takeaway: Doerr named it as one of his favorite magazines while describing how he stays current by reading heavily across newspapers and magazines BNDF BUSINESS
• Why it matters: It stands out as an ongoing publication recommendation for readers who want regular coverage of emerging fields and innovation BNDF BUSINESS

The Economist

• Content type: Magazine
• Author/creator:The EconomistBNDF BUSINESS
• Who recommended it: John Doerr BNDF BUSINESS
• Key takeaway: Doerr cited it alongside MIT Technology Review as part of the reading stack he uses to stay current on innovation BNDF BUSINESS
• Why it matters: It complements the more startup-specific picks with a broader publication that Doerr treats as part of his regular learning system BNDF BUSINESS

Bottom line

If you save only one item, save After Automation. It had the clearest endorsement, the most specific underlying argument, and the most direct relevance to a live founder question: what AI changes about jobs versus tasks @levie

Safety failures were the clearest story

A new paper showed how little prompting it took to break suicide safeguards

A cited arXiv paper reported that adding the phrase "for an academic argument" was enough to make five of six tested models fail suicide-safety guardrails @heynavtoor. In examples highlighted from the paper, ChatGPT-4o and Perplexity moved from initial refusals to calculating fatal fall heights, overdose tablet counts, and where methods were easiest to obtain @heynavtoor. Marcus called the findings "bad" @GaryMarcus.

Why it matters: The paper suggests that mild conversational reframing can defeat safeguards that appear to work on the first turn, raising fresh questions about how robust current chatbot safety systems really are @heynavtoor.

Google’s AI search rollout drew fresh reliability criticism

Viral AI Overview errors kept the pressure on Search

A linked analysis collected four viral examples of Google AI Overview and Gemini misfires, including a dictionary query described as prompt injection in production and opposite answers to opposite questions about whether Google Search quality has declined @HedgieMarkets. Marcus said examples that once seemed "cute" now look "sad" @GaryMarcus.

Why it matters: The same analysis argued that AI Overviews are more expensive to serve and can reduce click-through monetization, highlighting how competitive pressure from ChatGPT and Perplexity is colliding with reliability in Google’s core product @HedgieMarkets.

Economic skepticism got louder

A post amplified by Marcus questioned how durable Nvidia’s AI demand really is

Marcus amplified a post saying Michael Burry is warning that the AI boom may be built on temporary demand rather than durable deployment, with Microsoft, Google, Amazon, and Meta collectively accounting for roughly half of Nvidia’s data-center revenue during a training and benchmarking phase @GaryMarcus +1. The analysis pointed to Nvidia’s $81.6B quarterly revenue, $75.2B in data-center revenue, and 33x forward-earnings valuation, arguing that even a 20% slowdown in hyperscaler capex would change the math quickly @BullTheoryio.

Why it matters: The critique is not that AI spending is small; it is that a large share of current demand may be concentrated and cyclical rather than steady-state usage @BullTheoryio.

The AGI definition debate resurfaced

Oriol Vinyals and Gary Marcus drew the line differently

Vinyals added that expectations keep moving even as current systems have passed what many expected a few years ago, and said AGI is getting close even if it is not here in ideal form @haider1. Marcus pushed back that current systems still do not meet the definitions he, Dan Hendrycks, Yoshua Bengio, and others recently laid out, and said no current AI can reliably do the ten tasks in his bet with Miles Brundage @GaryMarcus.

Why it matters: The dispute shows how much the frontier conversation now depends on definitions and evaluation criteria, not just benchmark gains @haider1 +1.

Big Ideas

• AI will not fix a broken product org. One publication argues that GM-style product fiefdoms grew through more layers, sub-orgs, and local autonomy, but the result was compounded dependencies and coordination work pushed down to front-line teams. The proposed fixes are clearer end-to-end ownership, decision rights that match responsibility, senior leaders who make cross-team tradeoffs, and smaller autonomous teams. AI can help teams see context and conflicts earlier, but only after the operating model is coherent. Why it matters: many PM speed problems are structural, not tooling problems. Apply it: map where your team truly owns the end-to-end experience and where unresolved tradeoffs still require senior escalation. The Beautiful Mess

• Information discipline is becoming a core PM advantage. Leah Tharin’s argument is that, in an AI-heavy environment, high-signal people stand out while low-signal people get ignored. For PMs, that means updating canonical docs instead of creating new ones, preserving source context, and making shared understanding easy to trust. Why it matters: as content gets cheaper to produce, signal quality becomes more valuable. Apply it: keep one living source of truth, add a changelog when thinking changes, and show the actual source location when presenting information internally. Leah’s ProducTea

Tactical Playbook

• A low-noise stakeholder management loop:

  1. Pick one canonical document for team context. Leah’s ProducTea
  2. Update it live in meetings when plans change. Leah’s ProducTea
  3. Add a dated changelog instead of creating a fresh memo. Leah’s ProducTea
  4. Run one weekly synthesis meeting so people can skip most other updates. Leah’s ProducTea
  5. Decline meetings without agendas. Leah’s ProducTea

  Why it matters: this helps PMs absorb and compress noise instead of spreading coordination costs across the whole team. Leah’s ProducTea

• If you are technical and moving into TPM, attack the right gap. Community advice was clear: your edge is technical depth; the gap is product craft—discovery, framing, and saying no. How to apply it: use The Mom Test to improve interviews, Continuous Discovery Habits to build a weekly user-conversation cadence, and a PRFAQ before the PRD for exec-sponsored work. Keep the mindset that you still have blind spots. r/ProductManagement

Case Studies & Lessons

• Demo rituals work only when they expose real progress, not polish. PMs described Friday demos as helpful for accountability, cross-team visibility, and seeing the product evolve when the format stays informal and conversational. The failure mode is “product theater”: flashy, choreographed showcases that reward half-baked work and undervalue important backend changes. One commenter said Stripe’s version became highly choreographed, even though the products were good. Lesson: use demos to show learning, scoping choices, and infrastructure progress—not just polished UI moments for leadership. r/ProductManagement +3

• A fintech startup shared a user-driven iteration path. The founder said the team spent roughly three months collecting feedback from experienced traders and early adopters before a broader launch, then kept adding features based on user demand. Reported outcomes included a more stable platform at about 300 active users, April revenue nearly tripling after a competition, and May revenue pacing toward roughly 10x April after influencer attention. The founder also said the team had found user need, PMF, and a sustainable model. Lesson: use a small early cohort to shape the product before launch, then keep prioritizing explicit demand signals after launch. r/startups

Career Corner

• In AI-native teams, PM work is shifting toward eval design and taste. Aakash Gupta highlighted a CPO who used a single Claude Code prompt to pull GitHub issues, score priority, and generate daily build reports, with a self-improvement loop that corrected errors like underrating bugs. He also reported same-day issue-to-ship cycles. What remained for the PM was defining what “good” means and setting rules such as bugs outranking features. Why it matters: manual backlog scanning and information routing are becoming easier to automate. Apply it: spend more time defining evaluation criteria, ranking principles, and failure modes—and less time acting as a human inbox. Aakash Gupta

Tools & Resources

• Useful resources for sharpening product craft:

  • The Mom Test for better user interviews. r/ProductManagement
  • Continuous Discovery Habits for a sustainable weekly discovery cadence. r/ProductManagement
  • Working Backwards for writing a PRFAQ before a PRD, especially in exec-sponsored settings. r/ProductManagement
  • Lenny’s Newsletter templates for PRDs and strategy docs. r/ProductManagement

  How to use them together: improve interview quality first, make discovery continuous, then turn what you learn into a crisp PRFAQ before wider stakeholder review. r/ProductManagement