Your intelligence agent for what matters

1) Funding & Deals

• A new $30M hardtech fund targets earliest-stage industrial tech. Jakob Diepen announced an oversubscribed $30M fund for the earliest-stage hardtech founders building for critical industries. The launch messaging positions El Segundo as a destination for hardtech builders, and Mark Suster described Diepen as part of LA's early-stage hardtech push. @jakobdiepen +1

• Etched's early-2024 Series A came together around a full-stack inference thesis. The company says it raised about $100M after circulating a 30-page technical memo, even though every major Valley investor initially passed. The thesis was that modern AI inference required the full system — chip, boards, interconnects, cooling, and rack — not just a chip. Founders Rob and Gavin had dropped out of Harvard; Rob traces the urgency to GPT-4V flagging a tumor in an old photo, and Gavin brought kernel experience from Xnor and Octo. The round later compounded into follow-on support from existing backers. Invest Like The Best

2) Emerging Teams

• Screenpipe shows notable open-source traction in local agent perception. A market map posted to r/SideProject puts Screenpipe (YC S26) at 19,566 GitHub stars and actively shipping, versus OpenRecall at 2,874, with Microsoft's Recall bundled into Windows and OpenAI reportedly building something similar. The same post argues the stronger wedge is developer-facing agent infrastructure, not privacy-first consumer positioning. r/SideProject

• Lumbox is building infrastructure for the parts of agent workflows that usually still require hands. A solo founder says the product combines a real inbox for OTPs and verification links, a credential vault where plaintext never reaches the model, TOTP generation, and an MCP server with 100+ tools. The goal is to let an agent sign up, verify, store credentials, and log back in behind 2FA without human intervention. r/SideProject

• DVForge is an early traction signal in vertical hardware education. The founder, a recent electronics engineering graduate, built a LeetCode-style platform for chip design verification and reports 120+ signups in 24 hours, the highest-voted post in the target subreddit, and inbound from a venture scout. The UI is AI-assisted, but the founder says the practice problems come from textbooks and are verified by experienced engineers. r/SideProject

• Applied AI for SMB lead response is already showing narrow-workflow ROI. One founder says an AI agent that responds within 60 seconds, qualifies buyers, and books directly onto calendars helped a real-estate brokerage move from 2.3% to 6.1% conversion after five weeks of work on making the handoff to humans sound natural. r/EntrepreneurRideAlong

3) AI & Tech Breakthroughs

• Etched is making one of the strongest architectural claims in inference hardware. The company says its first-generation low-voltage inference technology runs at under half the voltage of other AI chips. It also says its custom interconnect cuts point-to-point latency by more than 5x relative to the Blackwell figure cited in the interview, making "cluster-scale memory" more usable for decode workloads. Etched is building the chip, rack, boards, cold plates, interconnects, and parts of production in-house. Invest Like The Best

• Software optimization still looks like the fastest path to more AI capacity. At RAAIS, ElevenLabs’ Angelos Peri said batching, fp8, speculative decoding, and kv-cache compression let the company serve 70x more users on the same GPUs. @nathanbenaich

• Local-model usability is improving at the tooling layer. Hugging Face now lets users filter public models by the hardware they already own; Clement Delangue says that still leaves 800k+ public models that fit on his M5 24GB via llama.cpp. @ClementDelangue

• Fusion crossed a public demo milestone. Vinod Khosla said RealtaFusion powered a lightbulb using electricity harvested directly from WHAM via direct electricity conversion, which he described as the first such public demonstration by a private company. @vkhosla

4) Market Signals

• Inference efficiency is improving quickly at the software and systems layer. Harry Stebbings says five founders — from 10-person startups to a $200B public company — reported cutting inference spend by 75% or more with little effort, no performance loss, and better latency. Nathan Benaich amplified a similar point from ElevenLabs, arguing that GPU scarcity is increasingly an engineering problem. @HarryStebbings +1

• Local AI is shifting from ideology to cost-control. Delangue cites a Stanford result that 71.3% of ChatGPT queries could be answered accurately by a local model and argues that a large share of enterprise AI work could run locally relative to frontier API costs, while also reducing dependence on rented models. @ClementDelangue

• The labor read-through from AI adoption remains more expansionary than contractionary. Ramp and Revelio Labs, analyzing more than 21,000 U.S. firms, found that heavy AI adopters grow headcount 10% and entry-level headcount 12% over the two years after adoption, with growth showing up after roughly 6-12 months. The caveat is that these adopters are more technical, higher-paying, and more likely to be venture-backed, and average AI spend is still only about $33.67 per employee per month. Big Technology

• Harmonic's Hot 25 remains a useful demand-side watchlist. ResolveAI reclaimed the top spot, Salient jumped to #2, aaruHQ ranked #3, Simile debuted at #6, and BrainCo_AI was the biggest mover at +12 places. @HarryStebbings

• Platform approvals are still a major choke point for applied AI. One restaurant voice-agent team says its production-ready system was rejected by Clover because the company lacks a policy for third-party AI applications, and separate attempts with Toast and Deliverect also failed to unlock distribution. In this case, integration approvals appear to be the gating issue rather than voice capability. r/SaaS

5) Worth Your Time

• Invest Like The Best: Etched founders — the best single source in this set for the inference-first hardware thesis: why they think inference is the largest market, how they recruit "legends," and why they chose chip-plus-rack vertical integration. Invest Like The Best

• Harmonic's Hot 25 Report — the full ranking behind ResolveAI at #1, Salient at #2, and aaruHQ at #3. @HarryStebbings

• Big Technology on AI adoption and hiring — a concise write-up of the Ramp/Revelio dataset covering 21,000+ firms, 10% headcount growth for heavy adopters, and the 6-12 month lag before benefits show up. Big Technology

• Clement Delangue on local AI — useful if you're tracking how much AI work can move from rented APIs to owned local models, and how Hugging Face is reducing discovery friction for that shift. @ClementDelangue

Top Stories

Why it matters: frontier competition is moving at three layers at once—model quality, inference economics, and specialized hardware.

• Anthropic launched Claude Sonnet 5. Anthropic says it is its most agentic Sonnet yet, with browser and terminal tool use, a 1M context window, and major gains over Sonnet 4.6 across reasoning, coding, and knowledge work; it is also the new default in Claude Code for Pro users @claudeai +2. The rollout was immediate across developer tools including GitHub Copilot, Cursor, and Devin @github +2. Artificial Analysis rated it #5 overall at 53 on its index, but said higher token usage can make benchmark task costs exceed Opus 4.8 @ArtificialAnlys.

• OpenAI reportedly found an optimization that more than halved inference costs. Reporting cited by multiple accounts says the technique reduced the GPU footprint for logged-out ChatGPT traffic to a couple hundred Nvidia GPUs at one point @steph_palazzolo +1. One cited analysis noted that lower serving costs could improve margins, raise usage limits, or ease API pricing pressure @kimmonismus.

• Etched emerged from stealth with an inference-first hardware push. The company says it completed A0 tapeout, built its first racks, signed $1B+ in customer contracts, raised $800 million, and saw early customer tests show SOTA throughput, latency, and power efficiency, with first racks shipping this summer @Etched. Etched also disclosed a low-voltage inference design it says can sustain 80%+ of peak FLOPs on trillion-parameter sparse MoEs without thermal throttling @Etched.

Research & Innovation

Why it matters: the most interesting technical work today focused on agent reliability, robotics transfer, and domestic compute adaptation.

• LongCat-2.0 looked more like an infrastructure milestone than a normal model release. A technical review of Meituan’s trillion-parameter MoE says training on Ascend 910 required coordinated changes across precision, kernels, memory, parallelism, reliability, and optimizer design, making the project a checkpoint for China’s domestic compute stack @ZhihuFrontier.

• ASPIRE proposes continual skill discovery for robots. The NVIDIA-led system continuously accumulates reusable sensorimotor skills instead of retraining monolithic policies, and reports up to ~10x lower transfer-learning token needs across multi-task, sim-to-real, and cross-embodiment transfer @guanzhi_wang +1.

• A new modularity paper argues LLMs organize themselves like brains do. Across 46 tasks, the authors say same-domain tasks recruit overlapping units while different domains recruit distinct ones; ablating domain-critical units cut accuracy by 26% in-domain versus 2.5% outside it @pengrui_han.

Products & Launches

Why it matters: product releases are increasingly packaging frontier models into workflows people can use immediately.

• Google shipped two new generative media models. Nano Banana 2 Lite is generally available for image generation in about 4 seconds at $0.034 per 1K images, while Gemini Omni Flash entered preview for conversational video generation and editing at $0.10 per second via AI Studio and the Gemini API @Google +1.

• Claude Science entered beta. Anthropic says the research app supports every stage of research with code-traced artifacts, on-demand environments, and optional connections to 60+ scientific databases @claudeai.

• Spellbook expanded from AI review into full contract operations. Its new Autonomous Contract Management product is positioned as end-to-end AI infrastructure for contracts, with the company saying it already serves about 5,000 customers across 80 countries @scottastevenson.

Industry Moves

Why it matters: the commercial center of gravity keeps broadening beyond the biggest labs.

• Moonshot AI’s Kimi reportedly reached $300 million ARR. The same report said API revenue now accounts for more than 70% of total revenue, and that a new round is underway at a $31.5 billion pre-money valuation @poezhao0605.

• Cambricon became China’s first trillion-RMB AI chip company. Its market cap reached RMB 1.013 trillion, while Q1 2026 revenue rose 159.6% year over year and net profit rose 185.0% @TechBuzzChina. The valuation is notable because IDC data cited in the same analysis put Cambricon’s 2025 China AI accelerator share at 2.9% @TechBuzzChina.

• Apify and Coinbase expanded x402 for autonomous agents. The partnership raises the number of purchasable web automation tools from about 2,000 to 20,000+, with no account, API key, or human in the loop required @apify +1.

Policy & Regulation

Why it matters: frontier model access is increasingly being negotiated through security controls, not just product roadmaps.

• The Department of Commerce lifted export controls on Claude Fable 5 and Mythos 5. Anthropic said it would begin restoring access the next day @AnthropicAI.

• Anthropic is redeploying Fable 5 with tighter cyber safeguards. After talks with the US government, the company said new classifiers will block more cybersecurity tasks, some routine coding and debugging will temporarily fall back to Opus 4.8, and it is expanding both government testing collaboration and an industry framework for assessing jailbreak severity @AnthropicAI.

Quick Takes

Why it matters: these smaller items still point to where tooling, deployment, and evaluation are moving.

• OpenAI introduced GeneBench-Pro, a benchmark for messy computational biology tasks that can take human experts 20–40 hours @OpenAI +1.
• vLLM v0.24.0 shipped with 571 commits, new model support including MiniMax-M3, and broad NVIDIA, AMD, Intel, CPU, and RISC-V optimizations @vllm_project +2.
• Figure 03 started performing a logistics workflow at BMW Group Plant Spartanburg @adcock_brett.
• Gemma 4 is now nearly 90% faster on Apple Silicon in Ollama via improved multi-token prediction with MLX @ollama.

🔥 TOP SIGNAL

Today’s clearest edge was better scaffolding, not more prompt poetry. Jason Zhou says a graph-backed map across three repos made his coding agent a lot smarter and cut token use by roughly 50%, while Mercari used Sourcegraph’s Agentic Batch Changes to take a GitHub Actions security fix from two repos to around 80 potential repos because the agent could reason per repo instead of doing brittle find-and-replace @jasonzhou1993 +2.

Same theme showed up elsewhere: Simon Willison is turning --help output into agent-usable instructions, and LATAM cut roughly 15% latency/token overhead by simplifying which agent formats the final answer simonwillison +1.

⚡ TRY THIS

• Give the agent a repo map before you ask for edits (Jason Zhou).

  1. Install Codebase Memory MCP and let it auto-index on first use.
  2. Start with get_architecture, then use search_graph + trace_pass for call chains and blast radius.
  3. Add a pre-tool-use hook so plain grep results get enriched with graph relationships even when the agent forgets the MCP-specific call.
     Jason says this setup cut token use about 50%; he also shows graph queries like files calling X without tests, and says giant codebases can index in minutes while smaller ones finish in seconds AI Jason. Study his setup skill: AI Builder Club skills@jasonzhou1993.

• Make the agent ship a demo, not just a diff (Simon Willison). Use this prompt skeleton inside the target repo: Review the changes on this branch → cd to ~/dev/shot-scraper and run uv run shot-scraper video --help → use that command to record a video demo of the new features against a local dev server and demo DBsimonwillison. Then have the agent write a storyboard.yml, start a local dev server, and record the feature flow with shot-scraper video. The timeless trick: make --help rich enough that it works like a bundled SKILL.md for the agent simonwillison.

• For org-wide fixes, seed the agent with one real repo pair, then fan out (Mercari on Sourcegraph). Mercari first fixed a GitHub Actions security issue on the Help Center frontend and backend, then extended the task to around 80 potential repos. The important part is not scripted search/replace: let the agent reason about each repo’s setup, react to CI, and push follow-up commits when needed @Sourcegraph. Announcement: sourcegraph.com/agentic-batch-changes@Sourcegraph.

• Treat out-of-scope traffic as product signal, not user failure (LATAM). Log real conversations from day one. LATAM used LangSmith to find two wins: moving final formatting to the supervisor cut roughly 15% latency/token overhead, and digging into the 13% out-of-context bucket revealed that 95% of those chats were legitimate needs like check-in, baggage, and benefits; adding a customer-care agent dropped that bucket to 1% and improved return rate by 6 points LangChain.

📡 WHAT SHIPPED

• Claude Sonnet 5 is landing fast. It is now in Cursor, where Cursor says it scores 57% on CursorBench versus Sonnet 4.6 at 49%; full rankings are at cursor.com/evals@cursor_ai +1. Simon Willison’s doc readout: 1M context window, 128k max output, adaptive thinking on by default, same tool surface as Sonnet 4.6, no temperature/top_p/top_k, and a tokenizer that produces roughly 30% more tokens than 4.6 simonwillison.

• Early Sonnet 5 verdict: more agentic, not automatically better. One same-day workflow recommendation suggested making it the default in Hermes/OpenClaw and Claude Code, while reserving Opus 4.8 for ultra-complex tasks @AlexFinn. But in Theo’s same-prompt game rebuild test, Opus 4.8 finished in about 26 minutes with the best result, GLM 5.2 took 35–40 minutes with no vision, and Sonnet 5 took roughly 2–2.5 hours, spawned many subagents, and produced the messiest build; his advice is to use smarter models for top-level routing and cheaper models for smaller subtasks so orchestration does not turn into token bloat Theo - t3․gg.

• shot-scraper 1.10 adds a video command that records browser demos from storyboard.yml via Playwright. Simon says GPT-5.5 xhigh in Codex Desktop built the feature, docs, and demo YAML from a prompt inside a repo checkout simonwillison. Docs: shot-scraper video. Release: shot-scraper 1.10simonwillison.

• Sourcegraph Agentic Batch Changes is in public beta: agent-driven batch edits across repos, free on Sourcegraph Cloud during beta, with self-hosted support coming July 8 in Sourcegraph 7.5 @Sourcegraph. Mercari used it in preview to scope and begin patching a GitHub Actions security issue across around 80 potential repos, and Canva used it to split batch changes by code ownership in a Bazel monorepo @Sourcegraph.

• Kody is Kent C. Dodds’ new OSS layer for turning existing coding agents into safer, deterministic integration and automation assistants rather than replacing them @kentcdodds +1. Repo: github.com/kentcdodds/kody@kentcdodds +1. Kent says it pairs well with Cursor cloud agents because those agents get a full machine environment; he used Kody’s Cloudflare + Kit connections to let Cursor fix missing SPF records and newsletter deliverability issues @kentcdodds +2.

• Harbor now integrates with Deep Agents, LangSmith Sandboxes, and LangSmith Observability for real, reproducible, isolated agent runs in parallel, with a deterministic check at the end @LangChain.

• Claude Desktop on Linux is now in beta for Ubuntu and Debian, adding desktop access to Claude Code, Claude Cowork, and chat on paid plans. Download: code.claude.com/docs/en/desktop-linux@ClaudeDevs +1.

🎬 GO DEEPER

• 2:48–3:28 — Jason Zhou on the useful query surface for graph memory. If you only watch one code-context clip today, watch this: get_architecture, search_graph, trace_pass, and a graph query for files calling X without tests in under a minute AI Jason.

• 3:57–4:27 — The pre-tool-use hook trick. This is the clever implementation detail: ordinary grep still works, but the agent quietly gets graph context injected into the result, so you do not depend on perfect tool choice AI Jason.

• 1:04–2:28 — Theo’s scheduled Devin regression loop. Good watch if you want agents doing daily QA, not just one-off coding: one top-level agent fans out page checks, subagents record evidence, and the whole run can be scheduled every day Theo - t3․gg.

• 6:22–7:29 — LATAM’s supervisor-only formatting fix. Short production lesson: if every specialist formats final answers, you may be paying a hidden tax in latency and tokens LangChain.

• Repos and resources worth reading end-to-end.AI Builder Club skills for Jason Zhou’s codebase-setup pattern @jasonzhou1993; Kody for deterministic agent automations @kentcdodds +1; shot-scraper video docs for agent-generated proof-of-work demos simonwillison.

Editorial take: today’s alpha was structural — graph context, reproducible environments, and proof-of-work loops beat raw model churn.

Practical deployment was the clearest theme

Today's most important updates were less about raw model novelty and more about where AI can be used, how it is constrained, and what kinds of work it can now handle. Anthropic's policy reset, new science-focused systems, faster media models, and sharper inference economics all point in that direction @AnthropicAI +3.

Anthropic restores Fable 5, but with a tighter safety perimeter

Commerce lifted export controls on Claude Fable 5 and Mythos 5, and Anthropic said it will begin restoring access tomorrow @AnthropicAI. Anthropic separately said Fable 5 returns globally with new classifiers aimed at blocking more cybersecurity tasks; some routine coding and debugging requests will fall back to Opus 4.8 while the company refines false positives, and it is drafting a common jailbreak-severity framework with Amazon, Microsoft, Google, and other partners while expanding pre-release testing work with the U.S. government @AnthropicAI.

Why it matters: This is a concrete example of frontier access reopening only alongside tighter safeguards and more formal coordination around misuse response @AnthropicAI.

AI for science is getting both better tooling and harder evaluation

Anthropic's Claude Science workbench lets scientists use natural language to run end-to-end research workflows, and it integrates NVIDIA's BioNeMo Agent Toolkit so agents can call accelerated genomics, single-cell, cheminformatics, and biomolecular tools inside the same environment nvidia. NVIDIA said 18 of the top 20 pharmaceutical companies already use BioNeMo, and Anthropic is taking Claude Science into public beta nvidia.

OpenAI, meanwhile, introduced GeneBench-Pro, a benchmark for whether agents can navigate messy biological data, choose analysis paths, and make judgment calls that computational biology research depends on; the tasks are framed as 20-40 hour problems for human experts, and Greg Brockman said GPT-5.6 Sol is a big step forward on the benchmark @OpenAI +1.

Why it matters: The science story is shifting from general assistant claims toward domain workflows and tougher task definitions that look more like real research nvidia +2.

Google pushed generative media further into low-latency product use

Google DeepMind shipped Nano Banana 2 Lite as its fastest and cheapest Gemini image model, with text-to-image generation in about four seconds for quick ideation; Logan Kilpatrick described it as under four seconds per image at $0.034 per 1,000 images @GoogleDeepMind +2. At the same time, Gemini Omni Flash became available in Google AI Studio, the Gemini API, and Gemini Enterprise Agent Platform for conversational video editing, multimodal input combination, real-world knowledge use, and linking text or graphics to video actions; Kilpatrick said it is state of the art for video editing at $0.10 per second @GoogleDeepMind +2.

Why it matters: Faster image generation, editable video, and image-to-video chaining in the same stack make media models more usable for iterative developer workflows, not just one-off demos @GoogleDeepMind +1.

Inference efficiency kept moving fast

NVIDIA said its Blackwell inference software stack cut token costs on DeepSeek V4 by up to 5x in about one month, while stacked optimizations such as disaggregated serving, large expert parallelism, NVFP4, and multi-token prediction raised token throughput per GPU by up to 20x nvidia. NVIDIA also cited deployments by Baseten, Cognition, Deep Infra, and Together AI, and said open-source frameworks including vLLM, SGLang, and PyTorch reached the same 5x performance gains on Blackwell over roughly the same period nvidia.

Why it matters: For teams tracking serving economics, deployable cost can now move materially within weeks, not just across hardware cycles nvidia.

Two research results showed agents moving deeper into hard reasoning and reusable skills

A simple LLM pipeline using GPT 5.5 Pro and Claude Opus 4.8 was reported to resolve nine open problems spanning theoretical computer science and commutative algebra; a separate description characterized the setup as a prover-verifier loop and said one of the solved problems had remained open for two years @binghuip +1. In robotics, NVIDIA GEAR Lab and collaborators introduced ASPIRE as an automated skill-discovery system that continuously accumulates reusable robot skills for multitask, sim-to-real, and cross-embodiment transfer, with up to a roughly 10x reduction in transfer-learning tokens and a gallery covering more than 150 tasks and more than 90 learned skills @DrJimFan +1.

Why it matters: The common thread is reuse: formal reasoning that can tackle open problems, and physical know-how that can transfer across tasks and hardware @binghuip +2.

Highest-signal pick

The clearest recommendation today was Kevin Systrom's mention of Competing Against Luck. It stood out because he attached a specific framework to it—jobs to be done—and tied that framework to Instagram's product thinking, including its core value of visual life-sharing and features like Stories MindVoice Production.

Competing Against Luck

• Content type: Book
• Author/creator: Clay Christensen
• Link/URL: Not provided in the source notes
• Recommendation context:YouTube interview
• Who recommended it: Kevin Systrom
• Key takeaway: Systrom highlighted the book's jobs to be done theory as a way to understand why users "hire" products, and said he applied that thinking to Instagram's value proposition and feature design MindVoice Production
• Why it matters: This was the most actionable recommendation in the batch because it came with both a reusable framework and a concrete founder example of how that framework shaped product decisions MindVoice Production

Two more worth saving

Steppenwolf

• Content type: Book
• Author/creator: Hesse
• Link/URL: Not provided in the source notes
• Recommendation context:X post
• Who recommended it: Martin Casado
• Key takeaway: Casado said rereading the book decades later made it feel like an entirely different work, because age changes the reader's perspective @martin_casado
• Why it matters: The value here is not a business framework but a reading practice: some books appear to reward rereading at a different stage of life @martin_casado

Linked report on foreign influence campaigns against American AI

• Content type: Report / article
• Author/creator: Not specified in the source notes
• Link/URL:btcpolicy.org article
• Recommendation context:X post
• Who recommended it: Marc Andreessen
• Key takeaway: Andreessen gave a direct endorsement of the linked report, telling readers simply to read it @pmarca +1
• Why it matters: It was the only recommendation in this batch focused on AI policy and influence operations rather than product thinking or literature @pmarca +1

Pattern

No repeat titles surfaced in this batch. The mix was still useful: one founder-endorsed product framework, one literary reread that gains meaning with age, and one policy report on AI influence campaigns MindVoice Production +3.

Big Ideas

• AI leverage is now a ladder, not a single skill. Colin Matthews frames it as personal, product, and systems leverage. On the personal side, PMs move from AI-written text to AI-generated artifacts to delegating full tasks through MCP-connected tools; on the product side, they move from disposable web prototypes to code-based prototypes in the real codebase to AI-generated PRs for small production changes Lenny's Newsletter. A matching signal from Lenny: the coordination-heavy PM role is fading in favor of prototyping with real code, querying data conversationally, and running coding agents @lennysan. Why it matters: leverage now depends on choosing the right rung for the job. How to apply: pick one recurring task and move it up one rung this week; when delegating analysis, require source citations in the output Lenny's Newsletter.

• Inclusive design starts at the edge, not the happy path. The inclusive design pyramid argues for starting with people who struggle most; if the product works for them, benefits cascade outward Mind the Product. The same talk pairs this with eat your greens: design for user needs over time, not just immediate wants Mind the Product. Why it matters: it reduces blind spots in accessibility and long-term outcomes. How to apply: start the next discovery cycle with edge-case users and ask what the user will need in two to five years, not just today.

Tactical Playbook

1. Replace stale dashboards with on-demand analysis. Ryan Hoover’s workflow: make sure the relevant data is in the database, have an agent write a skill to gather it, then ask the agent to analyze it and generate a temporary HTML dashboard @ryancarson. He argues dashboards decay in usage, while AI produces better insights when it has context and a clear goal rather than overly prescriptive instructions @ryancarson +1. Apply it: frame the question like an analyst brief, not a rigid spec; for example, ask whether users of a feature have higher 30-day retention and require cited sources in the output Lenny's Newsletter.

2. Run research short, sharp, and continuous. The Student Loans Company talk recommends quick sniff tests, frequent intercept sessions, and mining complaint logs, chat logs, and call listening for signals Mind the Product. Then package insights as stories with verbatims, audio, or photos using a situation → complication → result → recommendation flow Mind the Product.

Why it matters: faster signal collection only helps if stakeholders absorb it. How to apply: pair every important finding with one direct user quote and one recommended decision.

Case Studies & Lessons

• A research-backed comparison feature was dismissed, then became a defining feature five months later. In one product design project, comparison was documented as central to user decision-making, dismissed, and later turned into one of the product’s defining features r/ProductManagement. The deeper lesson was governance: the person who named a direction early kept authority, while the person with evidence had to keep re-justifying settled questions r/ProductManagement. How to apply: log major product decisions with the supporting evidence, the decision owner, and the condition that would justify reopening the call.

• Synthetic users are not a substitute for real users. The speaker argues synthetic users are shaped by biases in the underlying data and can mimic a person without capturing lived complexity or the hidden issues that surface only through rapport with real users Mind the Product. How to apply: use synthetic users only for rough exploration, then validate critical decisions with real people in context.

Career Corner

• AI PM hiring is rewarding evidence, not posturing. Aakash Gupta’s playbook: audit your resume for honest ML-in-the-loop work, learn fundamentals like evals, agents, and context, ship one real project, show it publicly, and target incumbents adding AI Aakash Gupta. Resume bullets should emphasize outcomes and numbers, not task lists Aakash Gupta +1. How to apply: one shipped project with a write-up of evals and failure modes is stronger than multiple tutorial clones Aakash Gupta.

• Tighten your tell-me-about-yourself answer. Hiring managers want to hear why your background fits this role, not a full biography r/ProductManagementJobs. A strong two-minute version opens with your current role and a measurable outcome, tells one relevant before/after story, and ends with why this role specifically r/ProductManagementJobs. How to apply: build one version per target role and let the deep dive happen in follow-up.

Tools & Resources

• Worth saving: Lenny’s guest post, How top PMs increase their leverage, plus the related course Become an AI-Native Builder. The course focuses on skills and MCPs for discovery, prototyping in the real codebase, shipping GitHub changes, and setting up evals @lennysan. Colin Matthews has taught AI and technical skills to PMs at companies including OpenAI, Google, Stripe, Figma, and Microsoft @lennysan.

• Prompt templates to reuse: a PostHog retention-analysis prompt that asks for cited sources and HTML cohort output, and a repo-generation prompt that creates a local mock-data prototype without backend dependencies Lenny's Newsletter.