Oliver Jack Dean @oledean

Why I've changed my mind on Europe: - There are a growing number of opportunities that are very specific to the geographic / cultural fragmentation on the continent. Cross-border payroll, FX led consumer banking, and multi-lingual voice models are great examples and obviously led to enormous outcomes already (deel / revolut / eleven). - Machine intelligence + maturing software markets are making for a much higher per-customer LTV than we've ever seen in the past, meaning you can unlock huge revenue scale with a much lower N count of customers .. 100M customers or even 10M customers supports multi-billion dollar outcomes. - Models are productivity miracles in a continent that desperately needs them. And the way this technology shows up in these countries can be very tailored to each country's ambition, i.e. can we make the NHS the best consumer experience in the world? What other parts of society feel intractably broken and could dramatically benefit from software? - Vibes are excellent and the depth of the ecosystem is growing - the ETN guys are mensches and teams like Ethos are silicon valley ambition / technical excellence / hustle culture. No qualifiers required. Sure there are plenty of challenges but for the first time in decades it feels like there are teams / tam / ambition / momentum to support global power law outcomes from the region.

Luke Knight @lukeknight

6 days ago

etn has raised $ 1.6M. From today, etn will be live 5-days a week out of our new 1500sqft studio in central London. In 10 months Ronan and I have scaled etn from just us 2, to 8 people. We’re building a team of people who feel as strongly as we do about building the tech network

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Reminder: price is set by storytelling in the short run, value is set by cash flows in the long run

Two-pizza culture was never about feeding hungry engineers. It was about ownership, speed, and not being bogged down by bureaucracy. Working backwards from the customer and putting pen to paper have always been our way of forcing clarity of thought. But the industry is changing, and it’s time to reconsider how we bring products to live. Read more: allthingsdistributed.com/2026/06/return…

EV / Data is the new EV / EBITDA

@MersenaryPE AIPE

Someone built a web tool that lets you design distributed systems and then break them on purpose to see what happens. It lets you drag and drop components to see how they handle real-world conditions like traffic, failures, latency, and scaling in real time. 100% free. Runs in your browser.

Context graphs will be the best way for businesses to enable and deploy agentic harnesses. There's a lot of context fragmentation across so many different tools in every company. A god-mode view that self-improves and self-organizes is the way tacit knowledge can be captured.

@LORWEN108 Cannot TM or other meditation relief help here? Curious to know?!

I keep saying it time and time again: it is very hard to automate workflows when those workflows live exclusively inside people's heads

Everybody is stepping into the LOD game, and so is LichtFeld Studio. Preview of 260M Gaussians streaming into the viewer live. I use the RAD format which is processed on GPU within LichtFeld. It can be also simply dumped straight into the spark.js web viewer, albeit it will die at that amount of Gaussians. What other solutions don't tell you is that they need hours to preprocess a 3DGS ply to make it streamable. This was just a ply exported to RAD by LichtFeld Studio's convert tool (took 5 min at that size) and it is immediately ready to stream. In the comments there is a smaller dataset with 103M Gaussians that streams on startup into the viewer. Both datasets were created by Andrii Shramko. Let's see how far I can push this (need bigger datasets)

Good take My guess is - demand for intelligence is near infinite - but 80% of workloads will be running on 99% cheaper models within 12-18 months - 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level ochestrator agents?) - rough analogy might be what % of macbooks or gaming PCs sold have the maxed out specs for CPU/GPU, prices are falling much faster than Moore's law here though - this leads me to think the limiting factor will be energy and compute, not better models At Coinbase we're working hard on routing prompts to cheaper models where appropriate, and in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.

Tommy @Shaughnessy119

2 months ago

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening - Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs - For real business use you have to move

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BREAKING NEWS: according to CloudFlare Radar Data, Agentic traffic has SURPASSED human traffic across the worldwide internet for HTML webpages.

NASA invented Systems Engineering -> SpaceX/SkunkWorks perfected it. Kelly Johnson wrote 14 rules, here are the 5 that matter most today: 1. Keep teams brutally small: "Restrict the number of people in an almost vicious manner. Use 10 to 25% compared to normal systems." More people doesn't mean faster. It means more coordination overhead and slower decisions. 2. Make changes easy + fast: "A very simple drawing and release system with great flexibility for making changes must be provided." Your change management system is either an enabler of speed or your biggest bottleneck. This is still the biggest slowdown in most programs today. 3. Record the important work. Skip the rest. "Minimum reports required, but important work must be recorded thoroughly." Not zero documentation. Not a thousand pages nobody reads. The right information, captured where it matters. 4. Test early and often. "The contractor must be delegated authority to test his final product in flight. He can and must test it in the initial stages." Test early. Don't spend three years in analysis before finding out your design doesn't work. Kelly was practicing iterative engineering 40 years before anyone called it agile. 5. Build daily trust across companies. "Mutual trust between the project organization and contractor, with close cooperation on a day to day basis. This cuts misunderstanding to an absolute minimum." Not quarterly reviews. Not 200 page status reports. Daily collaboration with shared context.

Greg Isenberg keeps saying AI businesses fail on client setup, not AI quality. Most builders will miss the actual opportunity. The money is not in the AI agent. It is in everything that gets the client to first value before they declare the project dead. Three onboarding checkpoints I use before the AI step: 1. Where does the client go to see it working? 2. Who decides if the first run is good enough? 3. What counts as the project being alive? A workflow that answers those three before it touches a single AI node is the product most builders are not building. What onboarding step do most builders skip first?

GREG ISENBERG @gregisenberg

2 months ago

I was once pitching in a board room at a top 3 VC firm for a $15M Series A. 12 people in the meeting. One of the GPs fully fell asleep. Out cold for 30+ minutes. Nobody acknowledged it. Everyone just kept going. I kept presenting my Series A slides to an unconscious man in a

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The next clue in AI reasoning: answers may be attractors. A new paper from Benhao Huang, Zhengyang Geng, and Zico Kolter introduces Equilibrium Reasoners (EqR) — a sharp mechanistic view of test-time scaling in latent reasoning models. The core idea is simple, but deep: Reasoning is not only generation. Reasoning can be convergence. EqR repeatedly updates a latent state. The authors hypothesize that generalizable reasoning emerges when training shapes the model’s latent dynamics so that stable attractors correspond to valid solutions. In other words, the answer is not merely “produced.” It is reached. This matters because test-time compute only helps when the model’s internal dynamics know how to use it. More iterations can improve reasoning — or make it worse — depending on whether the trajectory moves toward a solution-aligned attractor or falls into a spurious one. EqR scales along two axes: Depth: run more iterations so a trajectory can settle. Breadth: run multiple stochastic trajectories from different initializations and select/aggregate the ones that converge best. The first-page figure captures the punchline beautifully: training is capped at 16 iterations, yet the learned dynamics extrapolate beyond 1,024 iterations at test time. As fixed-point residual falls, accuracy rises. On Sudoku-Extreme, the paper reports a jump from 2.6% exact accuracy for feedforward models to over 99% with scalable latent reasoning — equivalent to unrolling up to ~40,000 layers. On Maze, EqR reaches 93.0%. But the benchmark is not the most interesting part. The most interesting part is the lens: Correct answers must become stable. They must be reachable. And convergence itself can become a signal. That gives the field a more precise language for test-time compute than “let the model think longer.” Not longer text. Not an external verifier. Not task-specific search priors. A learned attractor landscape. This feels important because modern AI is moving from static inference toward adaptive computation. The question is no longer only “how much compute should we spend?” It is: What internal dynamics make extra compute useful? Full credit to the authors: Benhao Huang, Zhengyang Geng, Zico Kolter. Paper: Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning arxiv.org/abs/2605.21488… I’m attaching the first page because Figure 1 is worth studying closely. The future of reasoning may not only be models that generate better answers. It may be models whose internal states learn where correct answers live — and how to converge there. #AIResearch #Reasoning #TestTimeCompute #DynamicalSystems #ArtificialIntelligence

We have, as far as I can tell, no good tests of the productivity impact of the autonomous coding tools that appeared starting in December 2025. Every paper out there is from prior to the Claude Code/Codex revolution. A huge gap in our knowledge about what is happening in coding.

A good FT piece from Martin Wolf arguing, rightly imv, that at root of UK's political woes is a 20 yr long slowdown in productivity growth "a good economy — one with widely shared economic growth — is a necessary condition for political stability in a liberal democracy"...

I built a helicopter game flying over manhattan in a few minutes with lovable's new google maps connector. you can try it, then go build something cooler. nyc-heli-ride.lovable.app

Lovable @Lovable

3 months ago

Lovable's Google connectors are live. You can now build full-stack apps that talk directly to data from Google: Gmail, Calendar, Drive, Sheets, Slides, Maps, Gemini Enterprise, and BigQuery.

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In my conversations with people about AI use, I've realised there's more nuance to how people are using AI than I initially thought. There are actually multiple stages of use, and some can quietly lead to AI psychosis, especially with sycophantic AIs. So I made a simple chart mapping the stages of AI adoption, from luddites "slop haters" to AI psychosis. It's useful for two things. First, knowing which stage you're in. Second, getting a read on the people around you, especially the ones who've drifted past stage 9 without realising it. I shared it with my team and found that most folks were at 4-7, with a few at 8 and 9s, which is a little concerning, but at least they are self aware of which stage they are in.

"When I go to sleep, there's like some AI breakthrough. When I wake up, there's some AI breakthrough, and by lunchtime, there's another AI breakthrough. It's pretty obvious that we're going to have AI that is vastly smarter than humans. I hope it's nice to us." — Elon Musk