Your intelligence agent for what matters

Funding & Deals

• Itera raised $12M from Costanoa, Colle, and Upfront. The company is building reconfigurable PCBs using liquid metal, with the core technical claim that boards can be reconfigured instantly rather than remaining fixed after fabrication @msuster +1.

• The bigger capital signal was agent infrastructure. Exa raised $250M at a $2.2B valuation for structured web search for AI agents, while OpenRouter announced a $150M round at a $1.3B valuation led by Capital G for a routing layer across 50+ models. These are not early-stage rounds, but they clarify where larger AI capital is moving: search, model switching, and cost control for enterprise agents 20VC with Harry Stebbings.

• Why it matters: one investor on the 20VC discussion argued these markets look more like developer tools than consumer platforms, implying room for a few large winners rather than a single dominant company 20VC with Harry Stebbings.

Emerging Teams

• Onyx Security is building a secure AI control plane for enterprises adopting autonomous agents. CEO Maxim Bar Kogan says the company trains models and agents to oversee other agents; co-founder Gil came from synthetic data and Nvidia, and much of the research engineering team came from an Israeli intelligence unit at the math/cyber intersection. The demand signal is notable: Onyx says autonomous coding agents and assistants already represent over 50% of what it sees in the average enterprise and are the fastest-growing category, often arriving without controls No Priors: AI, Machine Learning….

• KimptonAI is one of the cleaner finance-AI launches in the set. YC describes it as an AI-native terminal where agents do research and propose trades using real-time institutional-quality data, and says it is already powering billions in AUM. Founders are Jack Zumwalt, Adrian Delb, and Mauricio Ortiz @ycombinator.

• Finney is notable for execution discipline. The company says it started in early 2024 helping financial advisors source and manage clients. Its first customer offered to pay double for earlier access before there was a finished product, and the team later pivoted from a large prospect database to an outbound agent while keeping the core problem fixed. It also rolled back over-agentic functionality when hallucinations were too frequent, then reintroduced more autonomy later TechCrunch.

• Diffraction looks like one of the more credible deep-tech teams in the set. CEO Johannes Galazanos describes a first-of-kind quantum camera spun out of MIT work and emphasizes customer validation through LOIs, research grants, and early contracts rather than relying on technical novelty alone. The company explored microscopy and semiconductors before deciding space was a better initial market TechCrunch.

• Watchlist: vertical agents keep getting more end-to-end. YC highlighted Enjamb for agents spanning evidence synthesis, regulatory documentation, and statistical programming in drug development; Cignara for voice/chat agents that can resolve issues and execute governed, policy-bound actions for large B2C enterprises; and KugelAudio for multilingual voice AI that runs fully on-prem in customer Kubernetes clusters across 30+ languages and dialects @ycombinator +2.

AI & Tech Breakthroughs

• Hugging Face’s delta weight sync is the standout infrastructure advance. The team says async RL weight sync is now ~100x cheaper on bandwidth because roughly 99% of bf16 weights stay bit-identical between RL steps at typical learning rates. In TRL, only changed elements are encoded as sparse safetensors and transferred via Hugging Face Buckets to vLLM, taking per-step payload on Qwen3-0.6B from 1.2 GB to 20-35 MB. The demo ran fully disaggregated training across separate boxes and Spaces using only HTTPS and a Hub bucket, with no shared cluster, RDMA, VPN, or NCCL @ClementDelangue.

• Onyx’s oversight architecture is worth studying. Rather than putting a large model on every action, the company trains small specialized models to flag when a smarter agent should inspect a high-risk action, aiming to balance coverage, cost, and latency. The team also says understanding model weights and activations will be part of the longer-term solution to AI control No Priors: AI, Machine Learning….

• Runway is pushing video models into world models and physical AI. The company says it has been building visual foundation models for years, and is now trying to evolve from prompt-based video generation toward simulation systems that capture intuition about physics and human tasks, with applications beyond creativity in robotics and physical-AI infrastructure TechCrunch.

Market Signals

• The seed playbook is getting faster and less data-driven. Harry Stebbings argued that VCs now have to back higher valuations on less information because AI adoption curves move too fast, and that traditional seed investing has become a bottleneck versus wiring capital as breakout leaders emerge. TechCrunch’s StrictlyVC panel described a market where two founders with AI tooling can compress what used to require 10 people, two rounds, and a year of work into a couple of months, and some seed investors are deliberately backing founders in markets that do not even have names yet @HarryStebbings +1.

• Concentration risk is real. One investor said roughly three quarters of VC capital raised by companies over the last year went to five companies, and the same panel argued that optimism is still ahead of what the sector can show in the short to medium term. The long-term view in that discussion remained positive, but a correction was explicitly expected TechCrunch.

• Agent infrastructure is solidifying into its own layer. 20VC framed agent search, model routing, and related tools as the picks and shovels of the agentic buildout, especially as enterprises create internal agents that need current information and cheaper model selection. The market thesis is not pure winner-take-all: the expectation voiced there was a small number of meaningful developer-tool winners 20VC with Harry Stebbings.

• Open models are gaining real operating share, while compliance is moving into procurement. One usage datapoint said open-model use in Factory more than tripled relative to closed models over the last month by both consumption and event count. Separately, one SaaS founder reported receiving an EU security questionnaire with an AI Act compliance section and answered it with a reusable model-card style appendix covering data touched, training use, human review, retention, and vendors. That is anecdotal, but it is a useful sign that some AI procurement questions are starting to standardize @matanSF +2.

• Physical-world AI is moving closer to the center of the conversation. One VC on the TechCrunch panel argued that AI in the physical world is orders of magnitude larger than digital workflow automation, and Runway said it is actively trying to commercialize video/world-model research into robotics and physical-AI systems TechCrunch +1.

Worth Your Time

• Hugging Face on delta weight sync — the clearest primary-source write-up in the set on why disaggregated RL may get much cheaper to run and easier to distribute across ordinary infrastructure @ClementDelangue.

• StrictlyVC Athens: Three VC Perspectives on SpaceX, AI Valuation Fever and Where to Bet Next — useful for calibrating capital concentration, founder selection, and correction risk in the current AI market TechCrunch.

• No Priors: Building an AI Guardian for Enterprise with Onyx Security CEO Maxim Bar Kogan — worth the time if you are mapping the control layer around autonomous enterprise agents No Priors: AI, Machine Learning….

• Harry Stebbings on the new AI venture playbook — short, high-signal framing on why investors are paying more for less information in AI @HarryStebbings.

• KimptonAI YC launch — a concise primary source on the emerging AI-native terminal category in public-markets workflows @ycombinator.

🔥 TOP SIGNAL

• Claude Code's new dynamic workflows make orchestration itself a first-class feature: Claude writes a plan, fans work across 100s of subagents, verifies the results, and returns one answer @_catwu +1. That matches what background-agent builders are actually saying works today: Walden Yan describes practical multi-agent coding as a manager/subagent pattern with isolated boxes, not chaotic swarms, and Addy Osmani's warning is that verification, not generation, is the scarce skill now Latent Space +1.
• The real proof points were practical, not theoretical: Cat used workflows to audit hundreds of A/B flags in parallel in under 10 minutes, and Anthropic pointed to Jarred Sumner using them on Bun's Zig→Rust port at ~750k lines, 99.8% tests passing, in 11 days from first commit to merge @_catwu +1.

⚡ TRY THIS

• Use dynamic workflows only when the task is too wide for one pass. In Claude Code, turn on auto mode, then either mention workflow in the prompt or enable Ultra Code from the effort menu so Claude can decide when to fan work out. A good copyable use case from Cat: ask it to catalogue all feature flags and identify the ones stuck at 0% or 100% rollout; Boris Cherny says save workflows for token-heavy jobs like migrations, refactors, perf work, and batch bug fixes @_catwu +3.

• If you're building your own background agent, split control plane from sandbox on day 1. Walden Yan's pattern is to keep the brain outside the machine, scope secrets to the sandbox, and reuse existing dev boxes; Cole Murray's OpenInspect adds setup.sh hooks, pre-snapshots, and restore hooks so the agent starts from a ready environment. Use Docker Compose for infra parity, but expect full VMs when you need real app execution, nested virtualization, screenshots, or video-based testing Latent Space +1.

• Make the agent prove it before you believe it. Walden Yan says testing is an orchestration problem: the hard part is running the right services, flags, and sessions, not clicking pixels Latent Space. Addy Osmani's rule is to codify good with tests, user journeys, and visual regression, because generation is easy now and verification is not Beyond Coding. Mitchell Hashimoto's renderer story is the right cautionary baseline: the agent improved frame time from 88ms to 1.5ms, but a hand-written version still hit ~20µs with zero allocations, so don't merge just because the number got better @mitchellh.

• Route effort like you route models. Alex Albert's practical split: use /fast for interactive back-and-forth, normal mode for async work, and push to xhigh only for the longest or hardest jobs. That matters more now that Fast mode is roughly 2.5x faster and 3x cheaper, while Opus 4.8 defaults to high effort with about the same token spend as 4.7's default @alexalbert__ +3.

📡 WHAT SHIPPED

• Claude Opus 4.8 — Boris Cherny calls it Anthropic's strongest coding model yet: SWE-Bench Pro moved from 64.3 → 69.2 at the same price, and Cat says it's now the recommended daily model for Claude Code @bcherny +1. Simon Willison highlighted three agent-relevant changes: mid-conversation role: "system" messages, a lower prompt-cache minimum of 1,024 tokens, and Anthropic's own claim that 4.8 is about 4x less likely than 4.7 to let flawed code pass unremarked Simon Willison’s Newsletter.

• Claude Code dynamic workflows(research preview) — available across Claude Code CLI/Desktop/VS Code plus Anthropic API, Bedrock, Vertex AI, and Foundry; best in auto mode / Ultra Code Matthew Berman. Real usage examples today were strong: Cat's stale-flag cleanup in <10 minutes, plus the Bun Zig→Rust port case study @_catwu +1.

• Cursor + Opus 4.8 — shipped same day @cursor_ai. Cursor says 4.8 is more efficient and more persistent on harder tasks, while Theo says the updated CursorBench shows slightly worse raw performance than 4.7 but still within margin of error; Jediah Katz's live take is that it has "very good judgment" @cursor_ai +2.

• OpenInspect — Cole Murray open-sourced a cloud/background-agent foundation with Slack/GitHub triggers, screenshots/video, setup.sh + snapshot restore, an optional GitHub reviewer, and pluggable sandboxes. Repo: https://github.com/ColeMurray/background-agentsLatent Space +1.

• LangChain Deep Agents — Managed Deep Agents can run without a custom agent server, and Deep Agents v0.6 adds ContextHubBackend for versioned files that shape agent behavior from one run to the next @LangChain +2.

• OpenClaw — a nice open-source harness cleanup: cold turns 2.9x faster, warm turns 2.5x faster, tarball 59% smaller, deps down 42%. Blog: https://openclaw.ai/blog/lighter-core-sharper-claws/@openclaw.

🎬 GO DEEPER

• 20:55-22:23 — Walden Yan on why testing beats computer use. Best short clip today if you're still thinking coding agents fail because they can't click well enough. His point: real testing means orchestrating frontend, backend, services, flags, and session state correctly Latent Space.

• 13:39-14:55 — Walden Yan on separating the brain from the machine. Watch this before you decide between in-box and out-of-box agent architecture. It's the cleanest explanation in today's set for scoped secrets, permission boundaries, and why this design lets you reuse existing dev boxes Latent Space.

• 14:34-15:52 — Matthew Berman walking through dynamic workflows. Good quick explainer on the Anthropic pattern: one main agent plans, parallel subagents attack the problem from independent angles, and adversarial checks try to break the answer before it comes back Matthew Berman +1.

• 12:37-13:40 — Addy Osmani on cognitive debt. Worth watching as the day's best anti-hype check: over-relying on agents can erode muscle memory, and blind acceptance turns convenience into cognitive surrender Beyond Coding.

• Repo to study — OpenInspect / Background Agents. One of the clearest open-source references for a Slack-triggered background-agent stack with snapshots, reviewers, screenshots/video, and pluggable sandbox providers. Repo: https://github.com/ColeMurray/background-agentsLatent Space +1.

• Worth reading — Anthropic's dynamic workflows case study. If you want the official framing plus the Bun migration example, start here: http://claude.com/blog/introducing-dynamic-workflows-in-claude-code@_catwu +1.

Editorial take: the useful frontier is not "more agents" by itself — it's better orchestration, better environments, and much stricter verification.@_catwu +2

Top Stories

Why it matters: the day’s biggest signals were frontier model quality, commercial scale, and deeper investment in AI systems infrastructure.

• Anthropic launched Claude Opus 4.8. Anthropic says 4.8 improves judgment, honesty about its own progress, and long-horizon autonomy at the same price as 4.7 @claudeai. Third-party tracking put it at #1 on the Artificial Analysis Intelligence Index at 61.4 and #1 on GDPval-AA at 1,890 Elo, with an implied ~67% win rate over GPT-5.5 xhigh @ArtificialAnlys. Anthropic also reported 69.2% on SWE-bench Pro and about 4x fewer unremarked code flaws than 4.7 @ClaudeDevs.

• Anthropic paired the release with a massive financing update. The company said it raised $65B in Series H funding at a $965B post-money valuation, and that run-rate revenue crossed $47B earlier this month, driven by Claude deployments in core operations and everyday work @AnthropicAI +1. That makes this both a model launch and a scale signal.

• SpaceX says it is nearing a custom training stack for very large clusters. Musk said SpaceX has almost finished a C-based training stack that exact-maps to 220k GB300s with 800G NICs, uses heavy pipeline parallelism, and could deliver more than an order-of-magnitude speed improvement versus JAX on large training runs @elonmusk.

Research & Innovation

Why it matters: the most useful technical advances today focused on cheaper RL, faster visual grounding, and stronger search-based reasoning.

• Hugging Face cut async RL weight-sync bandwidth by roughly 100x. The key observation is that about 99% of bf16 weights stay bit-identical between RL steps; HF therefore transfers only sparse deltas. On Qwen3-0.6B, per-step payload fell from 1.2 GB to 20–35 MB, and the team demonstrated fully disaggregated RL over HTTPS and a single Hub bucket @ClementDelangue.

• NVIDIA Research released LocateAnything for faster object localization. The vision-language detector was trained on 138M high-quality samples and decodes bounding boxes in parallel instead of one coordinate at a time, improving localization accuracy and throughput for grounding and detection @NVIDIAAI.

• Harvard and MIT introduced Bidirectional Evolutionary Search (BES). BES combines forward search, backward decomposition into checkable sub-goals, and evolution-style recombination. It improved Llama-3.2-3B-Instruct on MuSiQue from 4.0% to 7.0% accuracy and beat other open-source evolutionary frameworks on circle packing and Heilbronn optimization @TheTuringPost.

Products & Launches

Why it matters: agents are moving closer to real work by plugging into codebases, Office apps, and open deployment stacks.

• Claude Code added Dynamic Workflows in research preview. Claude can now write an orchestration script on the fly, spin up hundreds of coordinated subagents, and verify results before returning them. Anthropic says this is for tasks like large migrations and service-wide investigations @ClaudeDevs +2.

• Perplexity Computer is now inside Microsoft Office. It is available in Excel, Word, PowerPoint, and Outlook, where users can draft documents, model, build decks, and handle email from a side panel. Perplexity says the product uses its enterprise security layer, including SAML SSO, audit logs, and granular admin controls @perplexity_ai +1.

• StepFun released Step 3.7 Flash as an open-weight agent model. The Apache 2.0 release is a 198B sparse MoE with ~11B active parameters, 256K context, 400 TPS, tool-use support, and benchmark wins including #1 on ClawEval-1.1 and SimpleVQA Search @StepFun_ai.

Industry Moves

Why it matters: enterprise AI spending is concentrating around context, infrastructure, and specialized new labs.

• Glean crossed $300M ARR. The company said it reached the milestone five months after $200M ARR and argued that enterprise AI’s moat is a strong context layer grounded in a company’s workflows, permissions, and systems. It said more than 85% of customers use Glean across five or more job functions @jainarvind.

• Hyperscaler AI capex remains on the same steep curve. Epoch said Q1 2026 spending came in at $156.1B, close to its $155.1B trendline, keeping projections of $770B in 2026 and more than $1T in 2027 intact @EpochAIResearch +1.

• Inherent launched with a $50M seed round. The new London-based Public Benefit Corporation says it is building AI agents that discover new knowledge and is explicitly organizing around recursive self-improvement of the research organization @inherent_labs.

Policy & Regulation

Why it matters: state-level AI governance is starting to turn into concrete compliance requirements.

• A cited report said Illinois SB315 passed 110-0, with provisions for third-party audits, transparency reports, risk frameworks, and whistleblower protections for frontier labs; the same report said OpenAI endorsed it @thursdai_pod.

Quick Takes

Why it matters: several smaller updates sharpened the picture on deployment, voice, and agent commerce.

• OpenAI shipped a new GPT-5.5 instant with improvements to sycophancy, factuality, and multilingual performance @michpokrass.
• Cartesia Ink-2 topped the new streaming STT benchmark for final accuracy at 3.59% WER and 0.21s latency @ArtificialAnlys.
• Elicit launched an MCP server so agents can search 138M+ papers and run full research reports inside Claude, ChatGPT, Copilot, Gemini, and other MCP tools @elicitorg.
• Google expanded Universal Commerce Protocol toward hotels, food ordering, and YouTube in the U.S. @Google.

A compact reading stack from Dan Loeb

In this source interview, Dan Loeb named four books that map across special situations, capital allocation, long-term quality, and focus under information overload Invest Like The Best.

Most compelling recommendation

You Can Be a Stock Market Genius

• Content type: Book
• Author/creator: Joel Greenblatt
• Link/URL: not provided in source
• Who recommended it: Dan Loeb
• Key takeaway: Loeb called it the best still-relevant book on event-driven investing and special situations, and said it shaped how he thought about spin-offs, demutualizations, privatizations, and post-reorg equities Invest Like The Best
• Why it matters: This was the strongest endorsement in the set. Loeb said many investors in that world still use it as a framework Invest Like The Best

Three adjacent books worth saving

The Outsiders

• Content type: Book
• Author/creator: not specified in source
• Link/URL: not provided in source
• Who recommended it: Dan Loeb
• Key takeaway: Loeb highlighted it as an influential book on capital allocation, focused on managers who pair capital allocation with strong operations, including examples such as Danaher and TransDigm Invest Like The Best
• Why it matters: It shows that Loeb's reading extends beyond situations and securities to the operating decisions of exceptional managers Invest Like The Best

Quality Investing

• Content type: Book
• Author/creator: Cunningham
• Link/URL: not provided in source
• Who recommended it: Dan Loeb
• Key takeaway: Loeb called it the most eye-opening book on owning high-quality businesses with strong moats and returns on capital for the long term Invest Like The Best
• Why it matters: In this set, it complements event-driven and capital-allocation thinking with a long-term quality lens Invest Like The Best

Essentialism

• Content type: Book
• Author/creator: not specified in source
• Link/URL: not provided in source
• Who recommended it: Dan Loeb
• Key takeaway: Loeb referenced it as a way to focus on what matters most while trying to ingest large amounts of information Invest Like The Best
• Why it matters: It is the only non-investing title in the group, making it the clearest signal that attention management matters alongside analytical skill Invest Like The Best

Why this set stands out

These recommendations came from Loeb describing books he found influential or useful in conversation. If you save one first, the clearest choice is You Can Be a Stock Market Genius because he paired conviction with specific application: he called it the best book in the area, said it remains relevant, and tied it directly to the framework he used for special situations Invest Like The Best.

What stood out

Today’s strongest signal was that AI is being treated more like infrastructure: huge capital at the top, more production-ready agent tooling in the middle, and tighter control requirements at the edge @AnthropicAI +3.

Anthropic set a new financing marker — even as AI spend gets harder to justify

Anthropic said it raised $65 billion in Series H at a $965 billion post-money valuation, and said the capital will fund research and expand capacity for Claude. The company also said run-rate revenue has crossed $47 billion, driven by Claude moving into core operations across industries and broader everyday use @AnthropicAI +1.

At the same time, enterprise cost discipline is getting sharper. Axios reported that companies are starting to question whether soaring AI spending is delivering meaningful returns, and cited an AI consultant describing a client that spent half a billion dollars in one month on Claude licenses after failing to set usage limits @axios.

Why it matters: Capital markets are still rewarding AI scale, but enterprise buyers are paying much closer attention to cost controls and return on spend @AnthropicAI +2.

The agent stack is starting to look like production infrastructure

OpenAI rolled out major Agents SDK updates: a Codex-style harness for long-running tasks, first-class sandbox support across multiple providers, a hosted shell and containers endpoint in the Responses API, agent memory, snapshotting and rehydration, handoffs, approvals, and TypeScript support alongside Python OpenAI.

OpenAI also emphasized separating the harness from compute so sandboxes can stay ephemeral while state is restored when work resumes OpenAI. That lands as background coding agents are becoming more practical: Latent Space described a December 2025 inflection when models such as Opus 4.5 and GPT 5.2 made spec-to-PR workflows viable with much less handholding, while Cognition said Devin’s merged PRs grew 7x and its share of commits rose from 16% in January to 80% in March Latent.Space.

Why it matters: The center of gravity is shifting from assistant-style chat in the IDE toward cloud-run agents that can keep state, use tools, and work over real files and environments for longer stretches OpenAI +1.

Enterprise adoption is pulling agent security forward

Onyx Security described a secure control plane that uses small specialized models to monitor autonomous agents and decide when a smarter review agent should step in, aiming to reduce illegitimate or incorrect actions without adding human review to every step No Priors: AI, Machine Learning….

Its CEO said that, in a typical enterprise, more than half of deployed agents are now autonomous coding agents, with another roughly 45% coming from low-code automations, and said the fastest-growing category often launches without controls No Priors: AI, Machine Learning…. He argued that traditional identity, endpoint, and API security tools lack the context to judge why an agent is taking a particular action, even as incidents have included downtime and accidental publication of code or tokens No Priors: AI, Machine Learning….

Why it matters: If autonomous agents keep spreading, oversight tools will increasingly need to understand intent and context, not just permissions and endpoints No Priors: AI, Machine Learning….

Mistral is pairing enterprise applications with sovereign infrastructure

Mistral framed high-end manufacturing as a major target market, arguing that useful models in this sector must understand physics, object dynamics, and factory optimization. The company also said it is building stack-wide sovereign infrastructure — from models and deployment engineering to controlled hosting capacity — and announced a new French data-center site on a roadmap to 200 MW by the end of 2027 and 1 GW by 2029 Bloomberg Television +1.

Related interviews said Mistral has already invested about €4 billion in data centers across France and Sweden, with CapEx plans in the multi-billion-euro range, and CEO Arthur Mensch said the company remains on track for $1 billion ARR in 2026, driven by software growth and infrastructure deployment CNBC International Live +1. Mensch also argued Europe has a short window to build independent AI infrastructure, calling AI a strategic and macroeconomic asset and pointing to limited chips, memory, and electricity as constraints CNBC International Live +1.

Why it matters: This is one of the clearest European cases for pairing industry-specific AI applications with domestic compute capacity and customizable models Bloomberg Television +2.

Also worth tracking

• Open-model momentum keeps building. NVIDIA said it is adopting the Linux Foundation’s OpenMDW framework across its open model families to simplify licensing, while Factory reported open-model use more than 3x higher relative to closed models over the last month and Fireworks said it is processing 30 trillion tokens per day with open-model share still climbing @NVIDIAAI +2.
• RL infrastructure is getting cheaper and more distributed. Hugging Face said its science team cut async RL weight-sync bandwidth by about 100x by sending only changed weights, reducing one example payload from 1.2 GB to 20–35 MB and demonstrating fully disaggregated training over HTTPS and a bucket rather than a shared cluster @ClementDelangue.
• NVIDIA highlighted sim-to-real robotics progress at ICRA. Among the results it cited: 3x faster multi-arm planning on Jetson with ScheduleStream, 4.5x better navigation success with COMPASS and roughly 80% real-world success with zero real training data, and around 75% real-robot grasp success for Grasp-MPC versus 41% baseline nvidia.

Big Ideas

• AI is collapsing the PM-design-engineering relay race. At OpenAI, PMs write markdown PRDs that Codex turns into shipped PRs; designers ship full UIs with instrumented noop backends; engineers focus on constraints, review agents, and build-failing tests in a shared repo Aakash Gupta. Gokul Rajaram’s “pod-of-one builder” describes the same direction: AI compresses execution, so the scarce skill becomes judgment—choosing the right problem and spotting mediocre output @sachinrekhi. Why it matters: coordination work is shrinking; product leverage is moving toward taste, validation design, and system constraints.
• The interface shift is from task-based work to agent orchestration. Emma from Resonant says PMs are becoming “agent orchestrators” who train agents over time so autonomous workflows do not turn into “AI slop” Mind the Product. Scott Belsky makes the product-level version of the same point: replace task-based workflows with ask-based workflows at the OS level, while keeping underlying models swappable beneath the UI @scottbelsky. Apply it: encode principles, examples, and guardrails in reusable artifacts agents can act on.

Tactical Playbook

• Use a painted door before you build the backend.

  1. Ship a real UI with a noop backend Aakash Gupta.
  2. Instrument clicks and flows to see where demand is real Aakash Gupta.
  3. Build APIs only where behavior justifies the investment.

  This gives product and design real evidence instead of speculative prioritization.

• Start AI products with evals, not giant prompt boxes. Lorikeet is moving from “give us your SOP” toward first defining what good and bad outcomes look like, then having a Coach agent generate SOPs, guardrails, and test cases Product Talk. When the model is close but missing one fact, they use “resolution in the loop”: the AI pauses for targeted human input instead of escalating the whole ticket Product Talk. Apply it: define failure cases, escalation rules, and knowledge gaps before you tune prompts.

Case Studies & Lessons

• Lorikeet followed customer pull, not founder intuition. The team spent months on reflection tools and information dashboards before a healthcare startup made the real job explicit: help clear the support inbox Product Talk +1. Their first prototype was a command-line workflow using real customer CSVs, which let them iterate quickly on real data Product Talk. Today the product runs a ticket-handling Concierge agent plus a Coach agent for configuration and improvement Product Talk +1. At scaled customers, human average handle time went up because people were now spending more time on the hardest tickets Product Talk. Takeaway: good AI automation can increase the value of human work rather than simply reduce it.
• AI can compress enterprise discovery into traceable artifacts. In the ACNA example, a messy $2.8B settlement with complex eligibility rules became a “First Pass” pre-validation layer that checks completeness before claims reach reviewers Product School. The workflow then turned those rules into decomposed user stories, test cases, and a living playbook Product School. Takeaway: in dense legal or operational domains, AI is most useful when it makes ambiguity auditable.

Career Corner

• Founder skills are becoming PM skills. Emma argues PMs are increasingly founder-like and well positioned to start companies Mind the Product. The gaps she highlights are practical: getting from prototype to credible demo, understanding fundraising dynamics, and choosing between sales-led and product-led growth Mind the Product. Her view: mildly technical PMs can now get to a real customer demo with tools like Lovable, and sales-led motions may be an easier way to get early traction than pure growth hacking Mind the Product. Apply it: build one demo yourself and be ready to talk about distribution, not just features.
• AI PM interviews are getting more technical. Exponent reports that Meta’s AI product sense round combines a traditional 30-minute product case with 30 minutes of live prototyping in Llama, followed by questions on token efficiency, latency, retrieval, and compute trade-offs Exponent.

Apply it: prepare both a product narrative and a technical one: what you would build, what data it needs, where performance trade-offs show up, and use real data sources when possible Exponent.

Tools & Resources

• Product Playbook pattern for AI discovery. The workflow moves from freeform idea exploration to vision, personas, JTBD, event flows, user stories, test cases, and lean canvas outputs, then stores them in a living “Product Playbook” that teams can revisit as markets change Product School. Worth exploring if your team keeps losing context across meetings and stale docs.
• Lovable for fast customer demos. Emma cites it as a practical bridge from rough prototype toward something a mildly technical PM can show customers for feedback, even if it is not yet production-grade Mind the Product.