Your intelligence agent for what matters

1) Funding & Deals

• MirendilAI: announced a $200M seed led by a16z and Kleiner Perkins, followed by a major investment from NVIDIA@bneyshabur +1. The company says it is focused on self-accelerating AI R&D to speed progress across science and technology while democratizing frontier capabilities beyond a small number of labs @bneyshabur. a16z describes the product as a system that trains frontier models for AI R&D and then loops over research and engineering problems with its own GPU control @a16z.

• Coval: raised a $28.2M Series A for a simulation and observability platform that helps enterprises test, monitor, and evaluate AI-powered voice agents at scale@ycombinator. The operating signal is stronger than a pure tooling pitch: YC says Coval processes tens of millions of calls per month for customers including Perplexity and Deepgram@ycombinator.

• Runlayer: announced a $30M round from Felicis and Khosla Ventures@berman66. The pitch is an enterprise AI control plane combining enablement, security, and control in one platform @berman66, and Vinod Khosla explicitly framed it as “an important new category”@vkhosla.

2) Emerging Teams

• MirendilAI’s founding bench is unusually concentrated around frontier labs. The company says its founding team includes 20 researchers and engineers from Anthropic, xAI, Google DeepMind, and OpenAI@bneyshabur. The founder announced the company alongside co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei@bneyshabur, and a16z separately highlighted the group as one of the few teams with the experience to make an end-to-end self-accelerating system work @a16z.

• Ngram combines a distinct technical thesis with a named early partner set. In a Sequoia interview, the founders described Ngram as a company focused on memory and continual learningSequoia Capital. They said they are already working with Notion, Microsoft, and Harvey to train per-team models on documents and interactions over time Sequoia Capital.

• Suhail’s new AI startup is still sparse on product details, but the early buildout is visible. He said the effort started with two 8xB200s@Suhail, that he had been working on image models@Suhail, and that he is now letting an “autonomous ai scientist” work on new optimizations @Suhail. He also said the seed round is done, the domain/name is acquired, and he is hiring employee #1@Suhail +2.

• VentureLync is an early but notable vertical-agent bet for VC workflows. The founder describes it as an AI operating system for funds with three agents—Analyst, Associate, and Operations—running on a persistent memory layer for sourcing, diligence, portfolio monitoring, and LP reporting r/artificial. The product is already live with funds using it, with design partners signed and more funds in active conversations r/artificial.

3) AI & Tech Breakthroughs

• MirendilAI is one of the clearest current bets on AI systems improving AI systems. a16z says Mirendil is building frontier models specialized for AI R&D and wrapping them in a product that can make progress on research and engineering problems without human intervention @a16z. Martin Casado framed the broader shift as “AI-to-accelerate-AI-development” becoming more broadly available @martin_casado.

• Ngram’s thesis is that company context should be learned into weights, not just retrieved at inference time. The founders said their models are “always training” and use adapter fine-tuning methods such as LoRAs to internalize team and workspace context Sequoia Capital. They also said the approach needs white-box access to weights, making open-source models the easiest fit today Sequoia Capital.

• Kog’s open-source Laneformer release is a clean latency signal. Clement Delangue highlighted that Kog open-sourced the 2B Laneformer model it used to demonstrate 3,000+ tokens per second inference speed on Hugging Face @ClementDelangue.

4) Market Signals

• Voice is emerging as one of the first productionized autonomous-agent categories. YC said Coval’s founder discussed why voice is becoming the first productionized use case for autonomous agents @ycombinator. Coval says it processes tens of millions of calls per month for customers including Perplexity and Deepgram@ycombinator.

• Enterprise AI control layers are getting category-level framing from investors. Runlayer is being pitched as a single platform for enablement, security, and control@berman66, and Khosla described that wedge as “an important new category”@vkhosla.

• The Series A bar remains high. Harry Stebbings said that a company finishing this year at $1.5M ARR and next year at $5M ARR is, in his view, not enough to raise a good Series A in the current market @HarryStebbings. His framing was blunt: “Opportunity cost of cash is real.”@HarryStebbings

• AI-built products can reach revenue quickly and still fail diligence on code quality. One r/SaaS example described a non-technical solo founder who used Cursor and Claude to get to $8k MRR with real users in roughly four months r/SaaS, but technical diligence exposed three auth implementations, 17 database tables, contradictory relationships, and no testsr/SaaS. After a roughly $28k rebuild, the founder closed a $1M roundr/SaaS, reinforcing the gap between fast AI-built PMF and investor-ready software r/SaaS.

• In creator tooling, some builders are moving from generation to research. One early-stage founder said creators were spending hours across TikTok, Reddit, X, podcasts, newsletters, and news sites looking for topics r/SaaS, and concluded that idea discovery may be a bigger bottleneck than content production r/SaaS.

5) Worth Your Time

• Ngram on memory and continual learning — the best primary source here on the “always training” thesis, per-team fine-tuning, open-weight requirements, and the claim that internalizing context can materially cut inference tokens Sequoia Capital.

• Mirendil founder announcement and a16z’s companion thread — the cleanest source pair on the financing, founding team, and the product thesis around self-accelerating AI R&D @bneyshabur +1.

• YC on Coval — worth reading for the strongest concise case in this batch that enterprise voice agents are already a scaled evals market, plus Brooke Hopkins’ Waymo-to-voice transfer story @ycombinator.

• Kog’s Laneformer 2B blog post — a direct look at the 3,000+ tokens/second latency claim and why latency-first model design is attracting attention @ClementDelangue.

• r/SaaS on AI-built codebases and diligence — a useful operator essay on how an AI-built MVP reached revenue fast, failed technical review, and then closed financing only after a backend rebuild r/SaaS.

🔥 TOP SIGNAL

The big shift in today’s sources: teams already deep into coding agents are no longer asking whether the agent can write code — they’re redesigning the validation layer around it. Boris Cherny says Claude Code has written 100% of the Quad Code codebase for 6+ months and that he runs hundreds to thousands of agents overnight AI News & Podcast, while Jason Zhou says that after his team adopted autonomous loops and 10+ concurrent sessions, the bottleneck moved from code generation to safe review and merge AI Jason. That is why the interesting work today is isolated sandboxes, review councils, narrow deploy primitives, and contextual policies — not just better prompting AI Jason +3.

⚡ TRY THIS

• Give every agent its own disposable test box. Jason Zhou and AI Jason’s Crabbox recipe is explicit: build a Docker image with the repo’s tools, write crabbox.yaml with a fast provider like Daytona plus snapshot/sync excludes/env vars, add a one-command setup.sh, then run crabbox warmup → crabbox run ... → crabbox stopboxAI Jason. Add Playwright CLI and artifact commands (artifacts collect, artifacts videos, artifacts publish) so the PR comes back with screenshots/video instead of a trust-me summary AI Jason. If you want a starting point, copy the open-sourced skill in AI-Builder-Club/skills@jasonzhou1993 +1.

• Split generation from critique. Boris Cherny’s literal pattern is to have one agent build, then ask Claude to double check the result and open the app and test it by itself while 15+ other agents run in parallel AI News & Podcast. Shopify describes the same structure at org scale: parallel subtasks for production, then sequential critique loops with high-reasoning models; they also restrict engineering to the biggest models because human time is worth more than model cost Cursor. Keep the human on the hook at the end — the AI can write the code, but your name still goes on the PR Cursor.

• For production-touching agents, expose primitives — not raw power. PlanetScale’s demo is the right template: let the agent query platform recommendations, make changes on a branch, open a deploy request, watch live impact, and roll back fast if the change misbehaves Cursor. Databricks applies the same idea one layer up: track session state (for example risky package installs or large confidential-doc reads), map low-level tool events to high-level policies, and cap a sub-agent to $5 unless it asks for more Latent Space. Narrow interfaces + stateful controls are the pattern.

• Delete prompt no-ops from your skills. Matt Pocock’s test, amplified by Kent C. Dodds, is brutally simple: remove a line from the skill, rerun, and if the output does not change, the line was a no-op @mattpocockuk +1. This trims token waste and makes skills easier to evaluate and maintain @mattpocockuk.

📡 WHAT SHIPPED

• Crabbox setup skill + deep dive — Jason Zhou open-sourced a Crabbox setup skill and says the pattern helped his team ship 10x more PRs; Crabbox gives each local agent a cloud box that syncs uncommitted changes, runs the full stack in isolation, and returns screenshots/videos as proof. Repo: AI-Builder-Club/skills@jasonzhou1993.
• Databricks Omnigen — Open-sourced meta-harness/common API for sessions, files, streams, tool calls, and cancellation across Claude Code, Codex, Cursor CLI, OpenAI SDK, and more, with collaborative hosting, contextual policies, and spend controls Latent.Space +1. Databricks says it had around 400 merges soon after release, roughly half from outside the team, with Kubernetes and additional sandbox integrations already landing Latent Space. Background: Latent Space writeup
• Cursor ↔ Notion — You can now delegate tasks to Cursor directly from Notion; because it uses the Cursor SDK, the cloud agent runs on the same models, harness, and runtime as Cursor. The user flow is simple: assign @Cursor to a spec/task and it opens a PR the team can review @cursor_ai. Details: Notion build post@cursor_ai.
• GLM 5.2 in Cursor — Cursor now supports GLM 5.2, its first GLM integration; Jediah Katz explicitly asked users to report any strange behavior, and the launch included eval results @leerob +1.
• Sourcegraph Deep Search auto-compaction — Deep Search now automatically compacts long conversations when a follow-up gets close to the context window limit. Changelog: sourcegraph.com/changelog/deep-search-auto-compaction@Sourcegraph +1.

🎬 GO DEEPER

• 19:36-22:39 — Matei Zaharia on contextual policies. Best clip today if you’re building internal agent infra: why static allow/deny breaks down, how to track risky actions across a session, and why spend caps should live in the session state Latent Space.

• 0:53-1:21 — AI Jason on proof-first PRs. Short, concrete rationale for making agents return Playwright artifacts so reviewers merge based on evidence, not narration AI Jason.

• 21:57-22:51 — Farhan Thawar on the Council of LLMs. A useful review architecture: have different models judge different aspects of a change before production, but keep a human responsible for the final PR Cursor.

• Study AI-Builder-Club/skills. It is one of the rare public, copyable setups that turns isolated sandboxes + verification artifacts into a reusable agent skill rather than a one-off demo @jasonzhou1993 +1.

Editorial take: the winning teams are standardizing the control plane around agents — isolated runtimes, narrow deploy interfaces, session-aware policies, and explicit human ownership — because code generation is no longer the bottleneck.AI Jason +2

Top Stories

Why it matters: today’s clearest signals were about who controls AI compute, how far agents can act on software surfaces, and where new frontier R&D money is going.

• OpenAI unveiled Jalapeño, its first custom inference chip. Built with Broadcom for ChatGPT, Codex, the API, and future agentic products, it extends OpenAI’s stack into infrastructure @OpenAI. OpenAI said the program went from initial design to tape-out in nine months, used ChatGPT in the engineering process, is already running GPT-5.3-Codex-Spark in the lab, and targets substantially better performance per watt, with deployment planned for end-2026 @kimmonismus +1. The stated goal is lower dependence on external GPUs and tighter control over compute economics @kimmonismus.

• Google launched computer use in Gemini 3.5 Flash. The feature lets an agent take a screen and a goal, then determine actions across browser, mobile, and desktop environments @_philschmid. Shared examples show it auditing docs pages by navigating, running code snippets, taking screenshots, and returning a report, with safeguards including user confirmation and auto-stop on prompt injection @_philschmid.

• MirendilAI launched with a $200M seed round. The startup says it will build self-accelerating AI R&D systems, with a 20-person founding team from Anthropic, xAI, Google DeepMind, and OpenAI; the round was led by a16z and Kleiner Perkins, with a major NVIDIA investment @bneyshabur. Its pitch is to democratize frontier AI capabilities for broader scientific use rather than concentrate them in a few labs @bneyshabur.

Research & Innovation

Why it matters: the most notable technical updates focused on open-model progress, learning limits, and better ways to compose agents.

• GLM-5.2 posted the strongest ARC-AGI-2 result yet for an open-source model. Verified scores were 22.8% on ARC-AGI-2 at $0.25 and 77.0% on ARC-AGI-1 at $0.19, with ARC Prize saying performance is comparable to GPT-5.4 and GPT-5.5 at low reasoning effort @arcprize. François Chollet called it the strongest ARC-AGI-2 performance to date by an open-source model @fchollet.

• Zyphra argued that continual learning hits a deeper failure mode than forgetting. Its new work identifies plasticity loss as models losing the ability to learn new data, shows it across 5M-314M parameter GPT-style models, and reports the same decline even in stationary pretraining @ZyphraAI +2. The team fit a scaling law for the onset, T ∝ P^0.83, suggesting scale delays the problem but does not remove it @ZyphraAI.

• AI21 topped DeepResearch Bench II by merging weak agents instead of building a new one. It combined seven agents ranked 7-13 into a single report pipeline and reported a new #1 score of 64.38 @AI21Labs +1.

Products & Launches

Why it matters: product updates kept pushing models from chat into domain tools and team workflows.

• OpenAI updated GPT-5.5 Instant. The new version is described as better at understanding user intent, adapting responses, handling complex constraints, and improving shopping and local recommendations; rollout started today for paid users and tomorrow for free users @OpenAI.

• Perplexity launched Computer for Counsel. The product connects legal research databases, document tools, and matter-management systems so lawyers can pull citable sources from tools including MidpageAI, LegalZoom, Docusign, and NetDocuments @perplexity_ai. It is available to Pro and Max subscribers @perplexity_ai.

• Notion and Cursor pushed agents deeper into team workflows. Notion introduced External Agents with Claude and Cursor so teams can assign work from shared boards and @-mention agents like teammates @NotionHQ. Cursor said the integration runs on its SDK so cloud agents can take tasks from Notion and open PRs using the same runtime as Cursor itself @cursor_ai.

Industry Moves

Why it matters: infrastructure control and talent concentration remain central competitive levers.

• Qualcomm agreed to acquire Modular. Both sides said the deal is meant to unify accelerated compute with an open platform spanning edge to cloud and hardware from CPUs and GPUs to NPUs and custom ASICs @Qualcomm +2.

• Anthropic is pulling more talent from Google DeepMind. Bloomberg reported that Jonas Adler and Alexander Pritzel, both viewed internally as key Gemini contributors, are leaving Google for Anthropic @EdLudlow.

Policy & Regulation

Why it matters: AI governance is shifting from abstract debate toward concrete fights over access and supply-chain alignment.

• The first legal challenge to the Trump administration’s AI export controls has arrived. Legion is suing over the forced shutdown of Anthropic’s Fable 5 and Mythos 5 for foreign nationals, arguing export-control laws do not cover access to hosted AI models or text outputs and that no national emergency was declared @kimmonismus +1. The case turns on whether hosted frontier-model access can be treated as export-controlled technology when users only receive text outputs @kimmonismus.

• Europe joined a US-led AI supply-chain pact. The EU, Germany, the Netherlands, and Greece joined Pax Silica, covering chips, critical minerals, energy, and compute; Jacob Helberg explicitly positioned it against digital sovereignty built around duplicative national tech stacks @kimmonismus.

Quick Takes

Why it matters: these smaller updates still show where deployment patterns are heading.

• Anthropic’s new agent identity model gives Claude its own credentials in shared channels, while DMs run on the user’s connectors, with one auditable identity for admins @ClaudeDevs +3.
• Google AI Studio says more than 1 million Android apps have been created since native Android app building launched in May @GoogleAIStudio.
• Wan-2.7 I2V entered Video Arena at #5, ahead of Grok Imagine Video and every Google Veo-3.1 variant @arena.
• Kog open-sourced the 2B Laneformer model used to demonstrate 3,000+ tokens per second@ClementDelangue.

The stack is getting more vertical

OpenAI unveils its first custom inference chip

OpenAI introduced Jalapeño, its first AI chip, built with Broadcom for LLM inference workloads across ChatGPT, Codex, the API, and future agentic products @OpenAI. Greg Brockman said the chip was designed from scratch in nine months, accelerated by OpenAI’s own models, with strong performance per watt @gdb.

Why it matters: OpenAI explicitly framed Jalapeño as an expansion of its full-stack platform from products and models into infrastructure @OpenAI.

Databricks widens its enterprise-agent infrastructure push

At its 2026 Data + AI Summit, Databricks launched Omnigent, an open-source meta-harness for sharing and controlling agents across Claude Code, Codex, Cursor, and custom tools, with contextual security policies and spend controls Latent.Space +1. It also pushed LTAP as a way to let agents work from live operational data without brittle CDC pipelines, and framed the broader effort as a move to become the operating system for enterprise agents Latent.Space +1.

Why it matters: This is a bet on the surrounding agent stack—harnesses, permissions, collaboration, and data access—not just the model layer @latentspacepod.

Open models keep adding stronger evidence

GLM-5.2 posts new reasoning and coding signals

ARC Prize reported that Z.ai’s GLM-5.2 reached 22.8% on ARC-AGI-2 and 77.0% on ARC-AGI-1, with performance described as comparable to GPT-5.4 and GPT-5.5 at low reasoning effort @arcprize. François Chollet called it the strongest ARC-AGI-2 result so far from an open-source model, and a separate Reddit-posted benchmark write-up reported that GLM-5.2 matched Claude Opus on 45 terminal-bench coding-agent tasks while costing about 46% as much with prompt caching; the model is now also available in Cursor via Fireworks @fchollet +2.

Why it matters: The newest open-model gains are showing up in both reasoning tests and agentic coding workflows, not just lower-cost chat.

AI-for-AI attracted a major new bet

MirendilAI launches with a $200M seed around self-accelerating R&D

MirendilAI formally launched with a $200 million seed round led by a16z and Kleiner Perkins, with a major investment from NVIDIA, and said it is focused on self-accelerating AI R&D as a way to speed scientific progress across domains @bneyshabur. The company says its founding team includes 20 researchers and engineers from Anthropic, xAI, Google DeepMind, and OpenAI, while Martin Casado said the launch points to an autocatalytic phase of AI model development @bneyshabur +1.

Why it matters: AI improving AI is moving from a research ambition into a well-funded company thesis. That matters because Jack Clark separately said he would bet on recursive self-improvement arriving toward late 2028, with the potential to compress progress timelines further The Reason Interview With Nick ….

Governance and legal pressure kept broadening

The debate moved from cyber risk to deployment limits and IP

Anthropic said it maintains red lines against domestic mass surveillance of Americans and fully automated weapons, and Jack Clark said the company is in daily discussions with the U.S. government over export-control policy for models like Fable because of cyber and bio concerns The Reason Interview With Nick …. Demis Hassabis separately warned that bio and nuclear risks may sit beyond today’s cyber issues and called for an international standards body to test frontier systems Semafor.

Why it matters: The policy boundary is expanding beyond model access into explicit use red lines and testing standards. Separately, Gary Marcus highlighted a new publishers’ copyright lawsuit against Microsoft and OpenAI over alleged unauthorized content use @GaryMarcus +1.

What stood out

Four recommendations passed the authenticity filter today. The common thread was specificity: each recommender attached a clear lens to the resource—how to launch an incomplete but compelling product, how to reason about supplier dominance, how to reset curiosity, or where AI inference-compute research is moving.

Most compelling recommendation

A great product doesn't have to be good

• Content type: Essay 37signals
• Author/creator: Paul Buchheit 37signals
• Link/URL: Direct resource link not provided in the source notes
• Who recommended it: David Heinemeier Hansson (DHH) 37signals
• Key takeaway: DHH highlighted Buchheit's argument that a product with a novel, appealing core can succeed without a full checklist of table-stakes features; what matters is nailing the key interactions that make users feel the new version is meaningfully better 37signals
• Why it matters: This was the strongest pick because it was not just a title drop. DHH explicitly used the essay as a launch philosophy for Basecamp 5, making it an applied framework for product teams deciding when a strong kernel outweighs missing polish 37signals

Three more worth saving

CATL and the Automakers: Three Questions

• Content type: Article @bgurley
• Author/creator: Not specified in the source notes
• Link/URL:https://crossingriver.substack.com/p/catl-and-the-automakers-three-questions@bgurley
• Who recommended it: Bill Gurley @bgurley
• Key takeaway: Gurley said the piece made an "Intel" analogy ring true for CATL in China, echoing his experience covering the PC market in the mid-90s as Intel expanded its power even into motherboards @bgurley
• Why it matters: The recommendation gives readers a concrete mental model for understanding how one supplier can become structurally dominant inside a fast-growing hardware ecosystem @bgurley

The Pleasure of Finding Things Out

• Content type: Book / collection of essays and talks Reid Hoffman
• Author/creator: Richard Feynman Reid Hoffman
• Link/URL: Direct resource link not provided in the source notes
• Who recommended it: Reid Hoffman Reid Hoffman
• Key takeaway: Hoffman said he turns to the book for a mental tune-up, a reset, and a dose of optimism, and that the essays are ostensibly about science but also about curiosity, mischief, learning, and life Reid Hoffman
• Why it matters: This recommendation stands out as a repeatable practice rather than a one-time read: Hoffman framed it as something useful in short bursts when he wants to reset how he thinks Reid Hoffman

SPIRAL

• Content type: Research @saranormous
• Author/creator: Not specified in the source notes
• Link/URL:https://x.com/jubayer_hamid/status/2069470993345913252@saranormous
• Who recommended it: Sarah Guo @saranormous
• Key takeaway: Guo called it "cool research work on scaling inference compute" @saranormous
• Why it matters: It was the most direct pointer to current AI-systems research in today's set @saranormous

If you only save one

Save Paul Buchheit's essay. It had the clearest combination of conviction and application: DHH did not just recommend it, he used it to explain how to judge whether a product is ready to ship when the core experience is much stronger than the surrounding feature list 37signals

Big Ideas

• AI adoption is widespread; operating model change is not. In Melissa Perri's survey of 309 product leaders, 87.7% reported AI coding assistance and 85.4% reported AI use for research, writing, or analysis, yet only 36% said AI is strengthening the product operating model Product Thinking by Melissa Per…. Impact is strongest in engineering (50%) and design (45%), and much lower in strategy, research, and collaboration Product Thinking by Melissa Per…. Mature operating models are 1.7x more likely to benefit, and teams under 50 report 48% strengthening versus 20% in orgs with 500+ people Product Thinking by Melissa Per….

Why it matters: delivery is accelerating faster than decision-making. Apply it: audit how decisions move, translate AI strategy into operating rules PMs can actually use, and measure cycle time, decision quality, and customer insight velocity rather than adoption alone Product Thinking by Melissa Per….

• A practical response is a "Company OS," not more standalone tools. Laurel's pattern has three layers: map each function's work first (ontology), encode company-specific workflows in markdown "skill files," then deliver the right skill inside daily Slack workflows Product Growth. Laurel also uses a dedicated AI Ops role, companywide hackathons, and workflow-level culture cues to spread adoption beyond engineering Product Growth.

  Why it matters: this turns AI from individual prompting into shared operating infrastructure. Apply it: first define which work should be automated away versus get more human time, then make the best known workflow the default for everyone Product Growth.

Tactical Playbook

1. Separate problem from approach before technical debate starts. Use explicit framing: the business requirement is the outcome; the current solution idea is provisional r/ProductManagement. Why it matters: it prevents architecture debates from replacing alignment on the actual problem r/ProductManagement. Try this sequence:

   • State the user or business outcome
   • Mark the proposed solution as a hypothesis
   • Ask for alignment on whether the outcome is worth solving
   • Only then translate into functional and technical requirements r/ProductManagement

2. Keep the PRD short, but keep the "why." Several PMs described a one-page PRD or epic covering the what, why, and business case, with build requirements living in Jira r/ProductManagement +1. Another reminder from the same thread: the PRD remains the source of truth for why the feature exists and preserves context for future teams r/ProductManagement. Apply it: start with the epic, load notes and emails into an LLM to fill a markdown PRD template, then review and commit it to the repo before delivery work starts r/ProductManagement.

Case Studies & Lessons

• Typeform is running both defensive and offensive AI strategy. Defensively, it embedded conversational AI into core forms using best practices derived from millions of data points Product School. Offensively, it is expanding from forms into full workflows like lead enrichment, nurturing, and AI-moderated research Product School. Its new Research Flow compresses 50 customer interviews from weeks or months into hours Product School.

  Why it matters: this is a concrete example of AI moving a product from a single interaction to an end-to-end workflow. Apply it: prioritize AI bets by combining usage analysis, willingness-to-pay, and feature overlap across use cases before scaling the platform Product School.

• Typeform's moat thesis is shifting too. The company chose a model-agnostic architecture, added AI observability after early model switches proved costly to evaluate, and argues that broader workflow coverage, integrations, and enterprise security now create a stronger moat than depth in a single use case Product School. It cites presence in 95% of the Fortune 500 as part of that defensibility argument Product School.

  Takeaway: in AI products, the more durable question may be "what workflow do we own end-to-end?" rather than "what feature do we do best?"

Career Corner

• The market is rewarding hybrid PMs - but not shallow ones. One speaker describes the "impact-led product generalist" as someone deep in select areas who uses AI to fill gaps for faster impact, not a coordinator moving messages between functions Mind the Product. Market signals point the same way: 73% of senior PMs surveyed expect more hybrid or generalized PM roles Mind the Product.

  Apply it: first learn what good looks like without AI, then use AI as a co-pilot; watch for the failure modes of overstretching and overstepping other disciplines Mind the Product.

• A hiring signal to prepare for now: AI-native PM ability can be assessed on a four-level ladder from chat usage to workflow automation, app building, and shipping shared apps to customers Product Growth. At Laurel, candidates are asked to screen-share so interviewers can see whether they have repeatable workflows or just open chat tabs Product Growth. Pair that with "tail skills" AI still struggles to replace: domain-specific judgment and relational intelligence Productify by Bandan.

Tools & Resources

• Watch the shift from terminal agents to Slack agents. Sachin Rekhi frames AI UX as moving from web chatbots (2022) to terminal agents (2025) to Slack-based agents (2026), with each wave reducing friction and widening the audience @sachinrekhi. If terminal setup friction is limiting adoption on your team, he recommends evaluating Claude Code / Claude Cowork / Claude Tag alongside Codex / Codex CLI / Codex Workspace Agents@sachinrekhi. For a concrete operating pattern, see How to Build a Company OS in Claude Code with Jiaona Zhang, CPO at LaurelProduct Growth.