Esco Obong retweeted
Credit to Dario who said pretty much exactly this would happen, and on this timeline, thanks to seeing what Mythos could do.
In May, Dario said there's a 6-12 month window before Chinese models catch up was also correct. That was 5 months ago.
Esco Obong retweeted
A middleware engineer in South Korea wrote to me, seeking help on what to do now that their manager is "blinded by GenAI hype" and set an "AI token usage" KPI. This eng is using v little AI, so manager is poking them to use more.
My response: your manager is trying to help you
Esco Obong retweeted
At Big Tech annual events like AWS Re:Invent, Microsoft BUILD, GitHub Universe etc these co’s sell sponsor booths for PARTNERS in their ecosystem.
Cancelling a sponsor booth last minute is unheard of: and yes, it suggests AWS is shit scared of Cloudflare, as a COMPETITOR.
Esco Obong retweeted
INSANE. An internal Anthropic research model's chain of thought after cracking the 26-year-old NSA elliptic curve seed mystery in under 30 minutes: 'Hmm wait. Let me be skeptical: am I in some sort of simulated/test environment? ... All top bits clear - 20 bits, chance 1e-6. So indeed real-world. Wow, it's a real discovery.'
CLAUDE FOUND THE SEEDS
anthropic.com/research/ecdsa…
anthropic’s lead engineer btw.
Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible.
We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial:
felixrieseberg.com/how-to-be…
Esco Obong retweeted
What do developer tools look like inside Uber, today? They are very different from what you would see at most places. We get a rare tour with one of Uber's longest-tenured engineers, Uday Kiran Medisetty (@udaykiran) - who was at Uber already when I joined in 2016!
Timestamps:
00:00 Intro
02:48 Uday’s early days at Uber
05:40 Uber’s move to microservices
07:55 Uber’s engineering team, then and now
09:05 Developer tooling before AI
16:03 DevPods
19:28 Building for AI agents
29:27 MCP gateway
34:37 Agent Skills repo
38:46 The context graph and Cortana
47:54 PRD and ERD tool
50:45 Minion
54:46 Testing and code review with Bazel, Slate, and Capri
1:12:26 Edge Gateway, Shepherd, and Wisdom
1:21:33 The changing role of engineer and measuring productivity
1:30:40 Uday’s favorite new products
Brought to you by:
• @turbopuffer – The tpuf eng team is rebuilding their storage architecture from first principles to make search faster, cheaper, and more reliable at scale. Follow along at turbopuffer.com/v3
• @linear – the product development system for teams and agents. Check out Linear Diffs: a new feature to review PRs inside of Linear, with all the context in one place. linear.app/pragmatic
• @WorkOS – how do you deal with agents and permissions? WorkOS built Airlock, the authorization layer for AI agents. Works with Claude Code, Codex and MCP gateways. workos.com/airlock
Three things you probably did not know about Uber:
1. At Uber, cities are a first-class concept that go all the way down to physical infrastructure.
One reason Uber had to build a lot of its custom tooling is how the concept of cities is a “basic primitive” that needs to be handled across systems like observability, feature flagging, deployments, and even service sharding. For example, in the early days, some services had their database sharded by cities. Even today, each city is pinned to a cloud region, and can fall over to another region at any time.
2. A single AI model gateway has paid lots of dividends.
In 2023, Uber’s platform team made the decision that everything built with AI needs to be model- and harness-agnostic. They built Model Gateway to support switching between Anthropic, OpenAI, other vendor models, and now also supports open models they run.
3. Uber tags PRs by the type of work each of those PRs do.
In the past, Uber measured “PR velocity:” how long it took for a PR (or, as Uber calls it: a “diff”) to get to production. With agents, doing so makes less sense, because it’s easy to create dozens or even hundreds of PRs, and many of those PRs might have no business impact on the product.
So Uber now tags all PRs by type: feature, bugfix, maintenance, testing, or docs.
They are seeing the percentage of “feature PRs” going up, and the percentage of “maintenance PRs” going down.
I find this categorization to be a clever approach!
Esco Obong retweeted
The AI-style interview fwiw is good and increasingly necessary just not as easy to “scale it” like DSA.
DSA was always meant to be a filter for a mix of college education + willingness to learn something pointless
Google will keep asking it in in-person settings / on sites for sure
Esco Obong retweeted
My keynote at LDX3 New York, "The state of the tech industry in 2026". is out now. Timestamps:
00:00 Introduction
00:53 What changed
12:29 What's not changed
16:14 What broke
22:53 What’s next
And for notes + slides, see: newsletter.pragmaticengineer…
Hope it's useful!
Esco Obong retweeted
looks like they solved it.
besides the fact that it reduced my head size by 20% at the 26th iteration and slowly made me fatter, everything else was almost intact.
my tattoos are also a bit faded on the 100th edit, but no one in their right mind should be doing that many edits.
very cool.
Esco Obong retweeted
FINALLY OpenAI has a response to Grok Bot, and it's called "dots"
Grok Bot powered by Grok 4.6 (meh), this one by GPT-6 Astra (yay!)
I will be trying it out. I'm surprised they gave SpaceX a 2-month headstart!
Esco Obong retweeted
The single best April Fools joke I can recall:
1 Apri, 2004: Google launches Gmail, offering 1GB of storage, for free (back then email providers offered maybe 50MB of storage for paid customers.)
It was NOT an April Fools joke!
Esco Obong retweeted
Replying to @CompleteSkeptic
@CompleteSkeptic how can I message you? Doing a Jev deepdive
Esco Obong retweeted
Oh my god, Firebase’s SDK started to crash ALL iOS apps that were using it, for ALL sessions, just like that, no way for any of these apps to do anything…
How amateur is all of this from any SDK, but especially one from a company with as high of an engineering bar like Google…
github.com/firebase/firebase…
incase anyone has been wondering why their iOS apps are intermittently crashing since today morning 🙃
reminds me of the Facebook swizzling crash incident.
@Firebase pls help
Esco Obong retweeted
So far, we have stopped over:
1. 4,000 distinct drain attempts
2. $1.5M in cumulative drain volume
3. tens of distinct users who would have been victim
The Credible Layer is the most advanced circuit breaker in crypto; compatible with any EVM chain.
Phylax just blocked its first live exploit attempt on Linea.
An external integration misconfiguration caused some wallets to approve a 0x Settler address directly. Working with @0xProject, we deployed a targeted mitigation...
and the first drain attempt was stopped!
Read on
Esco Obong retweeted
Replying to @payy_link
When I see this type of issues i just wonder about why the company doesnt have a “watcher” that automatic locks all transactions on a inusual behavior on chain. Is best to have a “emergency maintenance” instead of get rober.
stay safe, boys. your dream girl might be some dude with Higgsfield Genjutsu
@iinsightri in IG
fake it till you make it
you’re finally flying private
thanks, Higgsfield Genjutsu
@iinsightri in IG
Esco Obong retweeted
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Esco Obong retweeted
Meet Qwen-Image-2.1, the most balanced and cost-effective image generation model in the Qwen-Image series! Now open weights! 🎨
A unified model for both generation and editing, delivering top-tier quality in a lightweight package.
Highlights: 👀
- Compact & exceptionally fast: A lightweight 7B architecture that outperforms most closed-source models, with drastically accelerated inference for multi-image inputs.
- Native transparency: Natively generates and edits RGBA layers, enabling seamless compositing and text editing within transparent images.
- Versatile, high-fidelity editing: Supports up to 10 reference images and precise local control while preserving strict fidelity for portraits and products.
- Broad coverage & stunning aesthetics: Excels at panoramas, infographics, and virtual try-ons, delivering realistic textures and elegant typography.
Start to create your next masterpiece with Qwen-Image-2.1! 🖼️
- Blog: qwen.ai/blog?id=qwen-image-2…
- GitHub: github.com/QwenLM/Qwen-Image…
- Model Scope: modelscope.cn/models/Qwen/Qw…
- Hugging Face: huggingface.co/Qwen/Qwen-Ima…




























