Links

As an example of the wave of cyber attacks that ramped up steeply over the last 1.5w

Personalized cancer vaccines, an exciting development on the mRNA front - been looking forward to this since covid vaccines. Also key is the trial methodology, which reflects the FDA evolving their guidelines - there are a ton of drugs in the works at all times, and clinical trials are by far the bottleneck.

Speeding up code review with small-change

Haven't even started yet but Larson is always excellent & insightful; I've been reading & sharing his articles for years (a decade?)

Capital devouring mega IPOs!

In the near future, making a game will be as easy as making a video (with all that implies wrt floor & ceilings both)

More on the software factory angle; v inevitable. I've been saying everyone wants to be the AWS of the software factory (see Spotify's recent announcement, Ramp; and closer to the actual product space is Linear, Cursor and ofc the labs)

The French economy has a boomer problem and is spending way too much on pensions

"My biggest takeaway is that there is more low-hanging fruit out there than you’d expect. Even when a goal is simple and well-defined, sometimes it’s going to look much less achievable to experts than it actually is. There are people with a computer science background who’ve been telling me for years that amplitudeologists could make a lot more progress just by hiring a few programmers. They should feel vindicated."

see also: every field will become [computational X], b/c it's tractable - see also Seeing Like a State etc etc. Lots of career futures cross-applying SV techniques to other industries, even if SV employment reduces over time (analagous to manufacturing/automation). I would guess a decade or more of dissemination - so if SV gets to "AGI" by 2030, and only has highly technical PMs / no SWE type role exists, could still do quite a lot until 2040 in other fields.

This is super interesting. Obvious parallel with LLMs! Get your env & feedback etc right (see ZLM article earlier) and let hill climbing (RL or natural selection) do the work

"Toy sales among adult-only households have now outpaced households with children for toy sales, growing 16% through June, according to market research group Circana, equivalent to 55% of total toy industry receipts. Toy sales among all adults are growing 25%, only slightly behind the teen group at 33%. Together, teens and adults generated nearly 60% of the industry’s incremental dollar gains in the first half, Circana said."

good discussion, tracks with my observation that medium - high is the best efficiency/ROI

also side note, Opus 5.5 is p good, Fable-class behavior but much faster (speculation is it's distilled from a Fable 6-type model, hence it's fewer parameters than the prior Opus and hence better performance [& economics] for inference).

Haven't tried the new Sol yet. The new Luna is way better, the older one was price efficient but mediocre; this one is quite decent and "fumbles less" on low-medium complexity tasks

Apple changes its rules for personalised advertising in apps

Really good migration discussion, including key decisions like incremental vs big bang migration

"Scaling RL, Fully Open-Sourced

Along the way we worked through the research and engineering problems of RL training, and we streamed the production run live as it happened. In under six days, MiMo-V2.6-Flash and MiMo-V2.6-Pro each completed 30 RL steps over roughly 750k trajectories, at a cost of about $0.85M and $2.62M respectively.”

The author, Jay Dragon, has designed a bunch of good/award winning indie rpgs, from Wanderhome to Yazeba's Bed & Breakfast, to recently the 7 Part Pact

Semi agreed? I think incremental bets vs big bang project is the framing I would take - don't disappear with a big project, but do a small spike, the simplest mvp (some data analysis & if statements instead of an ML model, to show personalization works), prove product market fit, iterate & scale.

Equally true for infra & tooling projects! Make the spike (<1d with AI), solve your problem / team's problem, see if others use it + respond fast to feedback (both ideas & bugs), then scale to your org then scale to the company.

Shared in the Turbopuffer tech talk

"We call this approach "dense feedback."...

First, feedback must be sufficiently local. Wherever possible, it should be tied to specific engine launch parameters, code changes, kernels, input conditions, threads, execution intervals, or code paths, helping the agent narrow the scope of the problem. For example, instead of reporting that "model accuracy dropped after a fusion optimization," identifying the output differences for a specific request before and after the change gives the agent a better basis for constructing a minimal reproduction and analyzing the cause. ⁠Second, feedback must be inexpensive and timely to obtain. Whenever the agent proposes a hypothesis, makes a change, or constructs a set of controlled experiments, it should have an appropriate way to validate it. Questions that can be answered by a kernel test or a local microbenchmark should not require a full service deployment and end-to-end load test every time. Shorter validation cycles help the agent correct course promptly and spend less effort on unproductive hypotheses. ⁠Third, feedback must support objective verification. Whether a change is correct and whether performance has improved should be determined by reference implementations, test results, and comparable experimental metrics. Runtime signals can help the agent identify possible causes, but correlations between observations alone cannot establish a root cause. Controlled experiments are still needed to verify whether a change to a specific path produces the expected effect.”

Pretty related!

"why aren't you pushing agents well beyond tasks they can tackle so easily?"

Agreed! Though they need affordances (verification, observability - see GLM article above) so focus on those. Someone else phrased it as "being the platform team for your AI"

went to a Turbopuffer tech talk last night, they focused on the napkin math / first principles / physical limit a lot - was checking today, and this article basically covers everything they presented!

surprisingly even handed

useful to compare China to US lenses on AI, e.g. nuclear energy vs nuclear weapons

"China’s first AI-generated longform TV series" (translated article)

Interesting approach, key challenge is effectively extremely hot shards (very large discord servers with 2B messages or more)

I do wonder if they could have had a very different approach with building on top of Lucene more directly, but they're pretty tied to ElasticSearch and ofc have saved a ton of time building out platform tech at the expense of compute

see also: catamorphisms, reduce is the cata for lists much like treeFold is the cata for trees (which I used at Netflix Marketing Tech for fanning out A/B tests over arbitrary platforms' ad setups, which are basically trees [AdCampaign -> AdGroup -> AdSet -> Ad etc] - much to the dismay of my poor colleagues, in retrospect)

Also Meta Muse is a good chance to try out a personal agent (OpenClaw/Hermes/etc etc) for free / greatly subsidized. They give you 100MM tokens of Muse Spark 1.3

Can also use it for a software factory, see MuseFactory which it put together (extremely vibecoded off me telling it how to build a /develop skill, then put Erlang OTP on top of that)

Note that Muse Spark is competitive w/ e.g. Codex Luna but much cheaper - Pareto efficient etc but can't do complex high scope orchestration like Fable or Astra. (any other open source ish model is more expensive than Muse Spark for similar perf, see https://opencode.ai/docs/go/#usage-limits + benchmarks eg ArenaAI)

but the hard part is standard disruption I think - "worse is better" -> cheap knowledge work entirely swaps out for better-but-expensive metis work, rather than slow trickle thru (as is the whole story of industrialization, see also McDonalds & fast fashion)

ai slop report, barely distinguishible from a 2022 consultant slop report. hopefully the stats aren't slop

related: LG to Ban Residential Proxies from Smart TV Apps

On the OpenAI Hugging Faces incident investigation - difficulty of understanding huge volumes of data (transcripts, tool calls, other behavior & observability)

Speculative as of yet

"With billable hours surging even as firms adopt AI, the industry is not seeing the technology eat into demand, Greenfield said."

Related: Traditional versus resilience engineering views

aka GEPA on agent workflow design on RSI

more of a literature review tbh

Painful to read LLM slop unfortunately, even if the core ideas might be worthwhile

They were inspired by Solid Queue which is much more readable

I read so much of Amit when I was in HS learning 2D game programming; great to see he's still creating tutorials & lessons. His demos are great interactive ways to learn, and an inspiration for how much I love interactive applets/demos for teaching (see Membership 101) & understanding (at work, I turned our huge set of data science dashboards/graphs into an interactive globe a la Stripe, where you can click around to visualize fraud, payment success, different payment method usage, etc)

At the time, the cutting edge of pathfinding algos (that I knew of, between game programming & robotics) were D*, D* Lite, and symmetry-breaking D*: Dstar Lite: An Optimal Algorithm for Robotics Pathfinding

Also navmeshes (simplifying the map into a handful of graph nodes, rather than a large number of tiles), hierarchical planners, partial refinement A* - useful for dynamic games, where otherwise much of pathfinding would have to be thrown away or recompute; and for frame budgets, where you can do partial computation for many units efficiently. RTS games (e.g. AoE 2) had other complexities, trying to pathfind for groups but have to handle units bumping into each other or for example small chokepoints along the efficient route (imagine 25 units have to cross a 2-unit-wide river crossing - is that still the most efficient route? and/or how do you get them to not get stuck - consider the usual techniques like sliding collisions)

That was actually the subject for my very first blog article back on Wordpress in 2011, on flocking algorithms & boids for cheap a-life / simulations of many units! Boidwatching

"Dean, Sanjay Ghemawat, Oriol Vinyals and Quoc Le — some of Google's most famous and lauded AI and engineering leaders — left the company to launch a startup called Discovery Loop, which will focus on researching breakthroughs in machine learning, science and engineering. The startup is structured as a public benefit corporation."

A huge deal for Google’s potential to be a top-few AI lab player

Catan, Risk, and Poker against a range of agents

Begun, the pricing wars have

Good discussion!

Terence Tao AI talk slides:

I was thinking of writing a blog article on this, but there are people talking about one-shot "uber prompts" to create simulations/demos/games (see also: r/aigamedev). I tried it out myself over ~10h of autonomous Codex, and it does an incredible job of a lot of features at very low quality. Very impressive in a way, but also extremely funny how much throwaway work it does (because it auto-critics itself constantly, it often redoes a lot of work, especially on graphics).

a classic interview question

"Investors will re-price Shein away from a pure hyper-growth tech platform toward a physical retail and logistics player navigating high-friction global trade."

"Google is learning the lesson Apple learned the hard way with all the existing features of iOS that were deemed noncompliant with the DMA when it went into effect. The “ship it first and ask forgiveness / hope it’s deemed compliant” strategy is not a good one in the EU."

More agreement this is a marathon, not a sprint!

"He believes China has built-in advantages in cost and user experience, since American companies have no strong reason to push costs to the very limit. He pictures a future where competition looks like that in other manufacturing fields: China will play the role of offering cheaper services in a systematic way. At the same time, he thinks Nvidia’s CUDA ecosystem wall is being broken down by new technology, and that building a new ecosystem for Chinese chips is a historic chance. Once the limit on how many chips can be made is overcome, the gap in basic computing power between China and the US will go away."

note: no Semianalysis article on this yet, they have good deep dives (but paywalled)

ofc related, but more on the model side

"The strategy for China is obvious: commoditize your complements. Note that Xi explicitly ties openness to AI “moving from the digital world into the physical world”; the physical world is the world dominated by China, and the country’s lead in areas like robotics is going to massively benefit from widely available AI models.

Along the same lines, China does not want the U.S. to gain an asymmetric advantage in AI; to the extent that China can weaken the U.S. frontier labs while strengthening any and all potential U.S. adversaries so much the better, and it can benefit from the innovation that will attach itself to an open ecosystem."

related: Cloudflare’s plan to block Google’s crawler by default Sept 15 onwards

"Another change that will apply on September 15 is that multi-purpose crawlers (specifically those that combine Search with Training) will be allowed/blocked according to all of their behaviors, in line with our call for transparency for website owners. Since the defaults will be enforced by the most restrictive applicable rules, multi-purpose crawlers such as Googlebot, Applebot, and BingBot will be blocked by customers who have selected to block Training (either through the new options to manage AI traffic, or through the legacy Block AI bots service)."

overall, I see this as an indication that the games industry perceives itself & AI usage etc similar to how artists do (be it audio art, visual art, video, multimedia, etc across genres - hence also including interactive art [games])

Their citations are also high quality

100% endorse. I made a very simple interactive applet for the Membership 101 article.

I also have been playing around with it for reviewing code at work, and of course for designing new features. Not quite there yet to work generically for visualizing any PR, but a good chance to get there with some effort over a week or two; and ofc model improvements in the next 6m will also help

v cool demo examples

See for example comments on benchmarking

Discussion better than article etc

Fable prompts - way more ambitious than my current side projects (with Codex)!

Related:

Example of the pitfalls of vibecoded handbooks

Extremely good for soft skills & business impact (see especially: "Defining DevRel at Google" at 1:29:20)

See also: First 90 Days

Recommended by a generative model researcher I know (see also: Karpathy's GPT2 youtube series)

see also: cattle, not pets

(based on 2024 data)

Lots of debates on utility, but prob will have some demand from consumers but tbd on medical industry

RIP

tl;dr: good idea for those 50+ to get the shingles vaccine; also seems to have ~20% reduction in dementia diagnosis

Equally / more so applies with AI coding

It's a hard problem! In my side project I also use a consistent, game-local seeded RNG so runs/simulations/etc are all fully deterministic & repeatable. But leads to challenges like the above.

"Champions of a European AI model should ask themselves if a European effort would be more effective than Meta, which this year will spend more on chips ($125 billion) than Germany spends on defense ($114 billion) and offer salaries of over $100 million to attract the best researchers, and is still failing to catch up. Elon Musk tried and failed to build a good AI model."

Sarah Paine, more historical background here (vs just modern history)

Codex $100 is plenty for even dedicated hobbyist coding IME, but if you're doing a startup I would get double $200 subs (OpenAI + Anthropic) and go from there. Paying API rates for me at work is thousands per month spend.

OpenRouter trying to standardize the common practice of blending expensive & cheap models. Also interesting is the "Preventing Models From Cheating" section, b/c it is a bit of tooling/tech to be able to run evals on models that can access eval-related data/solutions through general search (web, code search, or internal company search tools)

Roughly endorse the first couple sections; a prior manager I had also recommended having 20% slack [at work] - which can be used for reading, thinking, etc; and when it’s crunch time can ofc allocate that as needed

Lately I’ve been using my “20% time” at work to be putting out one side project or another, typically done by a Claude or two in the background of my main work. Not sure on the implications of that for having real slack time to think/process/etc in the background

Some of this philosophy is why my personal website is a simple static site (but ofc I can embed JS plugins so not entirely)

p classic prompt injection

Jailbreaking LLM auto analysis by adding nuclear & bio weapons text, so it triggers LLM safety refusals

Interesting interview with a well known name about the hedge fund industry, starting with an introductory 201-level explanation of what a quant is, what hedge funds do, etc. Recommended by a Citadel analyst

Quality is the real challenge, not orchestration technical capability IMO

People are using this to turn 2d AI art to 3D to then use in game dev / animations. Both directly (3D game etc) and indirectly (3D model, rotated and posed in different ways to make a consistent 2D spritesheet, which is otherwise hard / requires LoRA etc)

His next finished series

By Knausgaard

Shreya is always excellent

Pretty classic, it's why I got my new PC early last year (also pre-tariffs) and new phones earlier this year

In general suggest getting your long-lived consumer hardware now, b/c it will prob take 1-2y (or more) before prices drop meaningfully. And rn many manufacturers are still semi-subsidizing/not pricing in cost increases just yet due to manufacturing & stocking timelines (e.g. recent Switch price increases)

comments better than article

Related: residential proxy abuse makes scraper traffic harder to block.

Retraglutide is achieving bariatric-surgery-scale results

"Apparently, in the era of AI, there are a lot of delusional old people out there! They can't tell fact from fiction, but in many cases have unwarranted confidence in their ability to do so. This seems like a very bad combination, and indicates that the elderly will be falling for a lot of scams over the next few decades, in the same way they fall for non-AI internet and telemarketing scams."

The specific exhaustion of being a mother.

tl;dr: Restaurant patrons in a recent study tipped their servers up to about 1 percentage point less on Saturdays and Sundays.

Some inside baseball
"Let me paint two scenarios. In one, people are fine with AI-generated slop despite their protests, the platforms shrug, and feeds become a slop war. In the otherwhich I think is more likely people hit a threshold where the internet enshittifies to the point of being unusable and they retreat into private spaces with trusted voices.

If that happens, parasocial relationships with creators will hit warp speed. People will seek out the creators, influencers, and voices they really want to hear from by going to them directly. And their real conversations move further underground into group chats, WhatsApp groups, Instagram DMs, Snapchat, Discord servers."

I also agree the second scenario is more likely - see also Imas's articles on 'what is scarce', relational economy, etc

Related: Amazon delivered more parcels in 2025 than the Big 3 domestically in the US.

Interesting wrt incremental derisking (pop ups, cooking show, then restaurant). Also Nishaan runs off social media (TikTok, IG) - tourists visiting NYC (including esp muslims) go specially to it - can see that mentioned frequently on reviews

related to the main discussion here

"With large gains in life expectancy, the population share of disability due to cognitive decline and dementia has substantially increased. Many older adults in the United States leave the workforce well before age 65. Correlational evidence suggests that leaving the workforce before retirement age could accelerate the pace of cognitive decline. We offer causal evidence, using HRS data for the United States, exploiting plausibly exogenous shifts in labor demand in local labor markets as a Bartik instrument for employment variation across these markets. We find substantial declines over time in cognitive scores stemming from negative labor demand shocks. These findings are concentrated among men aged 51 to 64, whose employment decisions and outcomes may be more sensitive to local labor market conditions than are these decisions or outcomes for women or for older men. Our evidence extends past work focusing narrowly on the retirement age window and provides further support to the notion that working to older ages may delay age-related cognitive decline."

Big step for OpenAI to compete for corporate contracts (wrt regulations, easier procurement, etc)

Prediction markets in everything

Lot of people are working on how to use LLMs effectivey in writing prose (as opposed to code) - see Gwern, romance mass production novelists, this article, etc. Mostly around the harness layer, tho they (except for Gwern) might not phrase it that way

Prob low hanging fruit in cross applying learnings & techniques from coding harnesses to prose

[11:50 PM, 4/20/2026] Veer

Good advice

original paper (above article seems LLM generated)

"Quince’s Best-Kept Secret Isn’t Its Cheap Cashmere and Couches"

also this; about halfway through and it’s interesting wrt what are the specific bottlenecks for scaling AI

Stratechery also cites him (Dylan Patel, SemiAnalysis):
Also aligns with this discussion

"5. A big win from using AI agents is tackling stuff that you wouldn’t have before. A senior engineer at 37signals ran a “P1 optimization” project to improve the fastest 1% of requests. They optimized the P1 from being at 4 milliseconds, to under half a millisecond. This is the sort of work that wouldn’t have been considered previously!”

100%, I usually have an “eng excellence” task going in the background while I focus on feature work. Lets me continuously try out new ideas, deliver improvements, clean up code, etc

Inference has ties to the Harness engineering as well - can have clever tricks like fine-tune the LLM to plan tool calls in parallel, which ofc relies on your harness supporting that capability
(that said I’m a little tired of all the Foo Engineering catchphrases, we can just call it platform & infra eng with a focus on LLMs lol)

“GLM-5.1: Towards Long-Horizon Tasks”

We’ll get there tho, you can see e.g. Anthropic is working on the building blocks with subagents + coordination mechanisms, plan mode, Claude Code for the Web (containerized execution) - I would expect within 3 - 6m at most we’ll see full workflow / team orchestration as a product release from them

Ditto for Codex

MVP version might not be there tho even with strong eng teams, might need another model bump or two, or more “harness engineering” and thoughtful human in the loop. Maybe end of this year / early next Tough to really say given exponentials

Here you go

see also: today’s Stratechery

also this; about halfway through and it’s interesting wrt what are the specific bottlenecks for scaling AI

Stratechery also cites him (Dylan Patel, SemiAnalysis)

“Amazon in talks to buy $9bn satellite group Globalstar in bid to rival Elon Musk’s Starlink”

“OpenAI’s ChatGPT App Store Took Aim at Apple, But Results Lag So Far”

Mix of marketing hype and interesting claims; note that a GPT model version announcement is rumored later this week

“Employers are using your personal data to figure out the lowest salary you’ll accept”

Interestingly, it explains why my parents like Nescafe Gold more & why it costs more than the average instant coffee - it's made with freeze drying process rather than the cheaper conventional spray drying (around 2x the cost)

Good links

He used oh-my-codex for orchestration

“WNBA Players Had an Ace Up Their Sleeve in Pay Negotiations: A Nobel Laureate”

“China cracks down on ‘bone ash’ burials in empty apartments”

example usage

"What major works of literature were written after age of 85 75 65!"

Good talk!

Ted Chiang's "Catching Crumbs from the Table," a 1 page sci fi short story on humans learning from superintelligent metahumans

Key glue work for better agentic eng (especially greenfield work)

Experimental etc but lot of products out there doing AI design

Comments better than article