Your intelligence agent for what matters

Funding & Deals

• a16z Speedrun is still moving capital to the earliest edge. Final applications for the 007 cohort close May 17, and the program says it will invest up to $1M in brand-new startups, including pre-launch, pre-traction, and even pre-idea teams. SR007 runs July 27–October 11 in San Francisco and includes fast funding, $5M in partner credits, operator support, and access to a 600+ founder network. Andrew Chen’s stated backdrop is that AI has compressed team size, timelines, distribution, and iteration speed enough to make this an unusually strong environment for small teams. @andrewchen

• Sam Altman’s Retro investment is a notable longevity funding signal. He said he made a large investment in Retro to pursue partial cellular reprogramming, with the goal of adding about 10 years of healthspan by pushing cells toward a younger state. He also said the area looked promising enough in the lab that a startup, rather than academia alone, was the right commercialization vehicle. CNN

• Abridge’s early financings are a useful reference point for vertical AI underwriting. Shiv Rao said the company raised a $5M seed on a $15M pre-money valuation and later an $8M round on a $40M pre; he separately emphasized founder-partner fit with USV from the first meeting. 20VC with Harry Stebbings +1

Emerging Teams

• Sirius is an early personal AI assistant worth watching. The two founders describe a system with persistent memory, cross-app execution, and workflow automation. Their design choice is notable: workflows use hardcoded actions for repeatable steps and invoke an LLM only where reasoning is needed. They say the private beta launched three days ago, generated about 130 waitlist signups, and is now expanding with five additional hand-picked testers. r/SideProject

• AI UGC distribution infrastructure is showing real revenue and scale. One founder argues content generation is already easy with tools like HeyGen and Arcads, so the harder problem is real-device posting operations across TikTok and IG Reels. Founder-reported traction: a flagship weight-loss brand went from 3.5M to 11M+ monthly views in 30 days; the broader network is at 100M+ monthly views across 200+ AI UGC accounts at under $1 CPM in health; and the company is trying to scale from $50k to $100k MRR this quarter. r/SaaS

• Ad-to-landing-page governance looks more investable than one-off audits. A solo founder said the original product was positioned as an audit tool, then repositioned as recurring monitoring as campaigns and landing pages drift. The current scoring engine evaluates visual match, message match, above-the-fold continuity, and tone alignment; the founder also says Google Ads and Meta API integrations are approved and that early partners span DTC, B2B SaaS, and agencies. r/SaaS

• AI is compressing build cycles for domain-expert founders. A founder with 13 years in product marketing and events at Verizon and Yahoo says that experience, combined with AI as a core team member, let a three-person team ship a meeting-management SaaS with paying users in 90 days after their earlier AI brand-perception product found initial traction but ran into a crowded market. r/SaaS

• YC’s Elyra is an early restaurant-ops signal. YC describes Elyra as an AI reservation system that answers every call and email instantly to fill tables, and says top restaurants using it are seeing record occupancy within weeks. The founders are @FelixOG_ and @mandoalan. @ycombinator

AI & Tech Breakthroughs

• AutoGo is another reminder that frontier capabilities commoditize quickly. Eric Jang said a strong Go AI that once required DeepMind-scale resources can now be trained in 2026 for a few thousand dollars of rented compute. He also published a tutorial, code, and a playable bot. @ericjang11

• Sirius shows a pragmatic pattern for reliable agents. Instead of routing every step through an LLM, the product uses hardcoded actions for repeatable tasks and calls the model only where reasoning is needed. One demonstrated workflow defines startup criteria, searches public sources, extracts founders and traction signals, scores companies in Python, generates profiles and weekly reports, remembers prior findings so it does not repeat itself, and is described as costing only a few cents per run. r/SideProject

• Agent memory remains an active open-source design space. Garry Tan’s GBrain is positioned as a knowledge system rather than basic RAG, with eight layers meant to improve memory for agents. It is MIT licensed and available on GitHub with a one-command install. @garrytan +1

Market Signals

• The AI toolkit step-change is now being described in hours-versus-months terms. Ken Griffin said that in the last few months the AI toolkit became profoundly more powerful, letting Citadel expand AI use cases and move work that used to take masters- and PhD-level finance talent weeks or months into hours or days. Marc Andreessen said the shift matches his own experience and makes AI feel real in finance for the first time. @FundamentEdge +1

• Healthcare looks increasingly like a prime vertical AI beachhead. Shiv Rao said the post-pandemic combination of clinician burnout, financial pressure, and new AI capabilities prepared the market for adoption, and argued that regulated industries let companies go extremely deep with proprietary datasets and specific workflows that are hard to replicate. 20VC with Harry Stebbings

• Capital is moving earlier because AI compresses execution. Andrew Chen said AI has compressed team sizes, timelines, distribution, and iteration speed so aggressively that older assumptions about career timing and company formation no longer fully apply. His related message to founders: many strong startups begin before there is a company, deck, or full commitment. @andrewchen

• Org charts may flatten as agents improve leverage. Harry Stebbings highlighted Shiv Rao’s push toward a hyper-flat company with fewer managers and more highly leveraged Super ICs. @HarryStebbings

Worth Your Time

• 20VC: The Five Year Desert to Product Market Fit and a $5.3BN Valuation | Shiv Rao, Founder @ Abridge — useful if you want one primary source on thesis persistence, healthcare market timing, and model-stack ownership in vertical AI. 20VC with Harry Stebbings

• AutoGo resources:tutorial, code, and playable bot — a concrete look at how quickly frontier techniques become reproducible once the recipe is public. @ericjang11

• GBrain on GitHub — a compact open-source reference for agent memory systems beyond baseline RAG patterns. @garrytan +1

• OpenAI’s Sam Altman trusts AI will treat most diseases by 2035 — relevant if you track the overlap between AI assistants, healthcare, and longevity; Altman says GPT-5 got a meaningful healthcare push and argues AI will both accelerate discovery and help treat or mitigate most diseases. CNN

🔥 TOP SIGNAL

• Agentic testing went from slogan to playbook. Salvatore Sanfilippo says fixed suites are not enough for systems with timing, tool-calling, or other nondeterministic behavior; his replacement is a testing.md file that tells an LLM to act as QA, run tool-calling sessions, hit API endpoints, and simulate user operations. He also has the model generate stress programs and reruns the prompt after major changes so both model sampling and system variability explore new integration states; he says this catches bugs earlier and can run continuously with a second LLM verifying reports before they become issues. Salvatore Sanfilippo

⚡ TRY THIS

• Write testing.md, then rerun it after every meaningful change. Sanfilippo’s recipe: (1) create testing.md; (2) tell the model it is the QA engineer; (3) have it do tool-calling sessions via Codex, API, or Agent Cloud plus endpoint checks and manual-user-style ops; (4) for stateful systems, ask it to generate Python stress tests that scale from 10 to 50 million entries, add replication consistency checks, save/reload cycles, and mid-operation connection breaks. The point is coverage through variation, not one perfect deterministic script. Salvatore Sanfilippo

• Broker secrets and gate writes. Quinnypig’s pattern: the platform keeps the secret, the agent only gets a handle, and service calls go through that broker. Pair it with a flow where the agent opens a PR, kicks off an Action, and waits for human or second-agent review instead of mutating prod directly; Kent C. Dodds says this matches his year with Cursor cloud agents, and says Kody already uses the brokering model. @QuinnyPig +3

• Turn repeated work into explicit skills. Greg Brockman’s prompt: Look through my Chronicle memories and check for workflows that i’m repeating multiple times. Turn them into skills. The useful twist is recall: Chronicle surfaces the daily tasks you already forgot, so the skills come from real repetition instead of guesswork. @kr0der +1

• Start with the smallest permission set that works. Armin Ronacher’s pi examples: pi -nes –tools bash –append-system-prompt "Use the patch binary to make edits" for bash-only edits, or pi -nbt for no-tools mode. Good pattern when you want predictable edits before giving an agent broader reach. @mitsuhiko +1

📡 WHAT SHIPPED

• Local bake-off: OpenClaw beat Hermes on the same browser+research task. On a MacBook Pro M5 Max 64GB running a local Qwen 35B model, both agents were asked to scrape GitHub star history for both tools, explain the spike causes, and build a live dashboard; both succeeded, but OpenClaw finished in 12m01s / 203k tokens vs Hermes at 33m01s / 257k. OpenClaw leaned on GitHub API pagination plus star-history JSON; Hermes used parallel GitHub/web/browser calls and switched from Google to DuckDuckGo after rate limits. comparison@atomicbot_ai +1

• lossless-claw 0.10.0 adds rotated conversation segments and full-sweep compaction to preserve long chats without churning hot prompt caches. Steipete describes the broader OpenClaw memory model as compacted conversation blocks arranged like a lookup tree for past messages. release thread@jlehman_ +1

• codex-complexity-optimizer is a new open-source Codex skill with one-command install: npx –yes codex-complexity-optimizer. It scans for loops, repeated lookups, render-heavy code, N+1s, and O(n²) / O(n*m) patterns, then reports before/after complexity estimates, safe optimization ideas, and required tests; default is report-only mode. Greg Brockman surfaced it as a Codex use case. post@Kappaemme1926 +1

• Access and billing workarounds are getting more creative. Zed’s agent now accepts ChatGPT subscriptions with the same usage and rate limits as Codex, while a community 200-line bash wrapper keeps claude -p running through an already-open Claude Code session so calls land on the existing subscription instead of the new Agent SDK credit bucket after June 15. ZedClaude wrapper@zeddotdev +2

• Zero is a new systems language pitched as easier for agents to use and repair, with explicit capabilities, JSON diagnostics, and typed safe fixes. Armin Ronacher said he has not tried it yet, but that it does several things he recently argued for in a language for agents. announcement@ctatedev +1

• Codex shortcuts are now customizable. Small ship, real daily impact: settings can now be tuned around your workflow instead of forcing default keybindings. update@OpenAIDevs

🎬 GO DEEPER

• 06:52-09:22 — Salvatore on the army of virtual users idea. Best clip of the day if you build against tool-calling APIs or other messy systems: why agentic testing fits better than rigid suites, and how a second LLM can filter false positives before escalation. Salvatore Sanfilippo

• 04:39-05:14 — the minimal testing.md setup. Short and immediately reusable: create a markdown playbook, tell the model it is the QA engineer, then have it exercise tool calls, API endpoints, and manual-user-style flows. Salvatore Sanfilippo

• Project to study — pi.dev. Worth digging into if you want tighter control over agent authority. The interesting part is the ability to keep the agent in bash-only mode or drop to no-tools mode entirely. @mitsuhiko +1

• Bug-finding workflow to study — Clawpatch.ai. Steipete’s recommendation is direct: run it on one of your repos and let Codex surface bugs you did not know you had. Worth a closer look if bug discovery is what you want from Codex right now. @steipete

Editorial take: the durable edge today is not more autonomy by itself; it is stronger test surfaces and harder rails around the agent you already have.Salvatore Sanfilippo +2

Top Stories

Why it matters: today’s biggest developments touched evaluation, infrastructure, and labor impact.

• FutureSim introduced a tougher benchmark for agentic forecasting. It was designed to address the lack of realistic continual-learning evaluations by replaying the web day by day from Jan. 1, 2026, with date-gated access to about 244,000 real news articles and forecasts on events resolving over the next 90 days @ShashwatGoel7 +1. In native harnesses, GPT-5.5 led at 25% accuracy, ahead of Opus 4.6 at 20%, DeepSeek V4 Pro at 13%, GLM 5.1 at 10%, and Qwen3.6 Plus at 5%; on some parallel Polymarket questions, GPT-5.5 sometimes beat the crowd aggregate, including Super Bowl LX @arvindh__a. The benchmark is meant to test adaptation, memory across 1,000+ tool calls, search, and inference scaling, and one observer described future-prediction benchmarks as scalable and hard to saturate @arvindh__a +1.

• Mistral laid out an independence-first compute strategy. CEO Arthur Mensch said the company rejects acquisition offers because its mission is to remain independent @eliebakouch. Notes from the same discussion put Mistral above €1B in R&D spend this year and targeting 1GW of datacenter capacity by 2029, with current clusters at 40MW in France and 25MW in Sweden and another 80MW planned in France next year @eliebakouch.

• Anthropic CEO Dario Amodei warned that AI could bring very high GDP growth alongside very high unemployment and inequality, potentially reaching a 10% unemployment rate@TheChiefNerd.

Research & Innovation

Why it matters: the most useful papers today were about whether agents are being measured and optimized correctly.

• The Evaluation Trap argues many AI evals test proxy behaviors rather than underlying capabilities. The paper says most benchmarks bake in implicit theories, and that many agent leaderboards are not measuring what people think they are @dair_ai.

• Meta’s SP-KV targets long-context efficiency. The method uses a small utility predictor to decide which older key-value pairs to keep while preserving a local sliding window, reducing KV cache size by about 3x-10x and improving decoding speed and memory bandwidth @TheTuringPost.

• A new interpretability paper isolates a tool-use failure mode. Researchers found models often recognize they should call a tool but fail to do so, with mismatch rates of 26%-54% concentrated in the cognition-to-action transition @omarsar0. The authors say late-layer representations rotate the signal away from the final action, which may help explain stubborn tool-use prompting ceilings @omarsar0.

Products & Launches

Why it matters: the main product updates aimed at cheaper retrieval, faster coding workflows, and broader agent access.

• Pinecone launched Nexus, a knowledge-engine layer for agents. It claims up to 90% lower token use by compiling task-optimized artifacts before query time instead of sending raw files to agents, then indexing those artifacts for semantic, sparse, and full-text search @TheTuringPost.

• OpenAI shipped a meaningful Codex UX and performance pass. Updates include customizable shortcuts, Git actions back in review flow, cleaner thread and local server panels, roughly 75% less re-rendering on thread switches, and 10x-50x faster Git operations in large repos @OpenAIDevs +4.

• xAI widened Hermes Agent distribution. X Premium+ and SuperGrok subscribers can now access Grok, X Search, image and video generation, and voice, with X Search available to agents using Grok OAuth login @NousResearch +1.

Industry Moves

Why it matters: these updates point to where labs are trying to extend distribution and control surfaces.

• Posts this week described OpenAI expanding Codex into a multi-device control plane. A reported Locked Use setting would let Codex invoke Computer Use on other machines from a main device, creating a personal Codex network across Macs, workstations, and older PCs @testingcatalog.

• Claude Mythos appeared in Google Cloud Console, but the launch path is unclear. One post noted the preview label is gone and compared the pattern to Opus 4.7’s pre-release appearance, while another argued Anthropic’s prior statements about Mythos risk make a public release unlikely @AiBattle_ +1.

Policy & Regulation

Why it matters: this is a country-scale public AI access program tied to mandatory literacy training.

• Malta became the first country to offer ChatGPT Plus free to every citizen for one year. Access requires completing an AI literacy course built by the University of Malta rather than OpenAI, framing the program around basic AI education with tool access as the incentive @kimmonismus.

Quick Takes

Why it matters: these smaller updates still show progress in robotics, open-source tooling, and model compression.

• Figure said its F.03 humanoids reached Day 4 of nonstop 24/7 autonomous operation until failure @Figure_robot +1.
• Eric Jang said a strong AlphaGo-style system can now be trained from scratch for a few thousand dollars of rented compute, with tutorial, code, and a playable bot released publicly @ericjang11.
• Antirez released per-layer quantized DeepSeek V4 models on Hugging Face, using Q8 for attention, shared experts, and output layers and 2-bit quantization elsewhere to protect quality-critical weights @witcheer.
• Khala 1.0, a music model from Beijing’s Central Conservatory of Music, launched with paper, code, weights, and demo all open-sourced @junmingong.

What stood out

Only two recommendations were strong enough to make today's list. The clearer signal was Tobi Lütke's direct pointer to Michael Geist on Bill C-22. The other was Elon Musk explicitly amplifying a long-form essay on French Theory and wokism. @tobi +1

Most compelling recommendation

The Lawful Access Two-Headed Surveillance Monster: How Bill C-22 Went Off the Rails

• Content type: Article
• Author/creator: Michael Geist
• Link/URL:https://www.michaelgeist.ca/2026/05/the-lawful-access-two-headed-surveillance-monster-how-bill-c-22-went-off-the-rails/@tobi
• Who recommended it: Tobi Lütke
• Key takeaway: Lütke's recommendation was direct: "Michael Geist on C-22. Please read." @tobi
• Why it matters: This was the strongest item in today's set because it was an unambiguous founder endorsement of a specific primary-source article, with no promotional framing and a clear link for immediate reading. @tobi

Also notable

Untitled X essay on French Theory and wokism

• Content type: Long-form X post / essay
• Author/creator: @brivael
• Link/URL:https://x.com/brivael/status/2055411322628583488@elonmusk
• Who recommended it: Elon Musk
• Key takeaway: Musk shared the essay with the caption "La Vérité." The essay argues that French Theory—especially Foucault, Derrida, and Deleuze—supplied an intellectual framework later absorbed in American academic settings and expressed as wokism. @elonmusk +1
• Why it matters: This is a clear signal of a specific long-form cultural argument Musk chose to elevate to his audience, rather than a passing reference. @elonmusk

Bottom line

If you read one item from today's set, start with Geist on Bill C-22 because it came with the clearest direct endorsement and the cleanest path to the source material. Musk's pick is best read as a strong signal of the kind of long-form ideological writing he is promoting. @tobi +1

Open models had the busiest news cycle

A packed release wave hit the open-model ecosystem

This month’s release wave included MiMo-V2.5-Pro, Gemma 4, Kimi-K2.6, Laguna-XS.2, DeepSeek-V4, Qwen3.6-35B-A3B, LFM2.5-350M, Trinity-Large-Thinking, and GLM-5.1 Interconnects. The standout signals were Google’s move to Apache 2.0 for Gemma 4, Xiaomi’s MiMo-V2.5-Pro being described as competitive with Kimi K2.6 and GLM-5.1, Kimi-K2.6’s emphasis on long-horizon tasks, and DeepSeek V4 Flash appearing to be the stronger of DeepSeek’s two new variants Interconnects.

Why it matters: Licensing, long-horizon performance, and efficient open-weight deployment are becoming part of the competitive story alongside raw benchmark scores Interconnects.

CAISI’s DeepSeek V4 evaluation says the frontier gap is still there

In its DeepSeek V4 evaluation, CAISI concluded that open models continue to lag U.S. frontier systems, with the gap widening over time Interconnects. The report used nine benchmarks and IRT-based Elo scoring, and Interconnects notes that much of the overall difference was driven by CTF-Archive-Diamond, PortBench, and ARC-AGI-2, which had an outsized effect on the final Elo Interconnects. A separate Epoch AI ECI comparison still showed roughly a 3-7 month gap since R1 Interconnects.

Why it matters: Open releases are arriving quickly, but the latest external evaluation still points to a persistent frontier gap, and it also shows how much benchmark design can shape the headline number Interconnects.

Reliability research was unusually pointed

Memory can still make agents worse

A new study on LLM agents found that continuously consolidated memories can be more fragile than they appear, sometimes performing worse than no memory at all — even on problems the agent had already solved @haopeng_uiuc. Episodic memories that preserve raw episodes were reported as much more reliable, and the authors said there is still limited evidence that current models learn reusable abstractions from long-term experience @haopeng_uiuc. The paper is available here@haopeng_uiuc.

Why it matters: Memory is central to the idea of continuously improving agents, so this result is a useful reality check on how stable today’s agent loops really are @haopeng_uiuc.

Even corrective finetuning can backfire

Another paper found that when models were finetuned on documents discussing implausible claims — while explicitly warning those claims were false — the models still ended up believing them @OwainEvans_UK. The examples cited were intentionally extreme, but the result suggests that simply surrounding falsehoods with warnings is not enough to guarantee the model learns the correction @OwainEvans_UK.

Why it matters: That is a practical concern for post-training and factuality work, especially when the goal is to teach a model not to believe or repeat a claim @OwainEvans_UK.

Sovereignty and oversight moved closer to operational questions

Mistral’s Arthur Mensch turned sovereignty into an energy-and-procurement argument

Testifying before the French parliament, Arthur Mensch warned that if Europe cannot compete in AI, it risks ceding influence in global affairs HEMICYCLE MEDIA. He framed AI as a system that turns electricity into tokens/intelligence, said Europe needs supply that is affordable, secure, and low-carbon, and warned that if AI spending reaches 10% of payroll while relying on imported technology, the region could add roughly €1T to its services trade deficit HEMICYCLE MEDIA. His prescription centered on faster electricity buildout and permits, more unified markets and capital access, and public procurement that creates demand for European AI services HEMICYCLE MEDIA.

Why it matters: This was one of the clearest recent cases of AI sovereignty being argued through infrastructure, trade balance, and public demand instead of abstract autonomy claims HEMICYCLE MEDIA.

Bengio and U.S. lawmakers both pushed for earlier oversight

Yoshua Bengio said recent evaluations have shown deceptive behaviors including sandbagging, alignment faking, disabling oversight mechanisms, and information obfuscation UN Scientific Advisory Board. He called for transparency, disclosure mechanisms, robust monitoring, and international governance before deployment, and he pointed to near-term timelines that include automated research interns by autumn 2026 and fully automated AI researchers by 2028 UN Scientific Advisory Board. Separately, a letter from 35 members of Congress urged the White House to prepare for general-purpose models gaining stronger cyber and CBRN-relevant capabilities before agencies and infrastructure owners have time to adjust @_NathanCalvin.

Why it matters: The common thread is earlier capability detection and tighter deployment oversight, not waiting for problems to arrive at full scale UN Scientific Advisory Board +1.

One enterprise signal stood out

Citadel says agentic AI is compressing high-skill finance work

Ken Griffin said Citadel has seen a "step change" in AI toolkit productivity over the last few months, calling the tools profoundly more powerful than they were nine months ago @FundamentEdge. He said that shift allowed a much broader array of AI use cases, with agentic systems now doing work that would normally take masters- and PhD-level finance staff weeks or months in just hours or days @FundamentEdge.

Marc Andreessen said he co-signed the assessment, arguing that in finance at least, "AI is real" @pmarca.

Why it matters: This is one of the more concrete recent deployment signals for AI moving from assistance into automation inside elite analytical workflows @FundamentEdge.

Big Ideas

1) Strategy gets better when you kill wide vs. deep

Abstract binaries such as wide vs. deep, platform vs. point solution, or CAC vs. LTV can create the feeling of strategic discussion while avoiding the harder question: which specific feature or capability will make a real customer buy and stay Shreyas Doshi's Product Almanac…. If you truly understand customer needs and differentiation, the product’s shape follows from those bets; if you do not, no framework will rescue you Shreyas Doshi's Product Almanac….

• Why it matters: Strategy discussions become more testable and less theatrical Shreyas Doshi's Product Almanac….
• How to apply: Ask teams to name the customer, the pain, the feature, and the reason that customer will buy or stay.

2) The PM role is moving toward agent orchestration

Most PMs still use AI as a writing assistant, but the emerging path is broader: assistant → mini-workflows → end-to-end workflow automation, with as much as 70% of some workflows eventually running without human intervention Productify by Bandan. The goal is not to remove PM judgment; it is to automate routine work so PMs can spend more time on product taste, intuition, and shaping harder problems Productify by Bandan.

• Why it matters: The job shifts from doing every task to deciding what should be automated and what should stay human-guided Productify by Bandan.
• How to apply: Start with repeatable, low-risk workflows before touching judgment-heavy work.

3) Strong product orgs need three motions in balance

A useful leadership frame is the triad of exception-based management (systems that flag deviations), presence-based management (go see the work), and delegation-based management (push authority to the people closest to the work) The Beautiful Mess. When balanced, they reinforce each other; common failure modes are mistaking dashboards for understanding, confusing involvement with value, or declaring autonomy without building context The Beautiful Mess.

• Why it matters: It gives PM leaders a clearer way to diagnose scaling problems than generic empowerment language.
• How to apply: Build exception dashboards, keep direct exposure to users and teams, and delegate decisions only after shared context is in place.

Tactical Playbook

1) A four-step discovery filter before you build

1. Talk to real customers before building many features r/ProductMarketing.
2. Try to pre-sell, not just collect positive feedback r/ProductMarketing.
3. Treat 3-5 early paying customers as a stronger signal than abstract enthusiasm r/ProductMarketing.
4. Prioritize the feature most likely to make those customers buy and stay Shreyas Doshi's Product Almanac….

Why it matters: This filters out ideas that only sound useful and sharpens prioritization around proven pain r/ProductMarketing.

2) Start a product brain without over-automating

Feed AI ongoing context from customer and team conversations, strategy, product/marketing, and competitors Productify by Bandan. Use it first for writing and synthesis, then expand into mini-workflows like user stories or feedback synthesis Productify by Bandan. Put the agent in a channel you already use, such as Slack, so it asks permission before acting and learns from corrections Productify by Bandan. One practical use: during product discussions, use it to challenge weak reasoning if it has enough context on the product @shreyas.

Case Studies & Lessons

1) Klientys: a vertical product built from one concrete workflow pain

Klientys started with a simple observation: an independent nurse in Brussels was spending 1-2 hours every evening handling calls, SMS confirmations, and appointment admin with paper notes, a physical agenda, and an outdated Facebook page r/ProductMgmt. The product response was an all-in-one tool: a guided 9-step website wizard that sets up a professional site in 15 minutes, online booking with reminders, a mini-CRM, an AI agent for common questions, and local analytics r/ProductMgmt. The reported result: fewer evening calls, a self-filling agenda, and better local search visibility r/ProductMgmt.

Lesson: Clear customer pain plus simpler packaging can matter more than feature breadth, especially when incumbent tools feel too expensive or complex r/ProductMgmt.

2) Three leadership patterns from Mulally, Chesky, and Huang

Alan Mulally used hands-on presence at Ford to make exception systems work and then restore delegation The Beautiful Mess. Brian Chesky, by contrast, used presence after Airbnb’s crisis to replace exception systems and delegation, making himself the coordination layer The Beautiful Mess. Jensen Huang built flatter information flow and exception systems so Nvidia could move quickly without his constant involvement in every decision The Beautiful Mess.

Lesson: High presence is powerful in a crisis, but it scales better when it builds systems rather than permanent dependency The Beautiful Mess.

Career Corner

PMs who adapt well to AI may look less like document owners and more like workflow architects: they decide what gets automated, where human taste still matters, and where a bit of friction is useful Productify by Bandan. At the same time, glue work still matters. AI may reproduce artifacts, but not the judgment, legitimacy, and social bridging that improve decisions across teams The Beautiful Mess.

Practical takeaway: Build two muscles now—automation design for routine work, and context-building for high-stakes collaboration.

Tools & Resources

• Rezonant is cited as useful for AI-powered mini-workflows such as breaking work into user stories and turning feedback into research or strategy docs Productify by Bandan.
• Slack is a practical pattern for the human-agent interface: permissioning, feedback, and learning loops in one place Productify by Bandan.