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From write-through to write-back, learn when and why to use caching to speed up your systems, reduce costs, and keep users happy.
Read the latest here: https://t.co/E4LJkf5s0l
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Okay this one's genuinely exciting!
Every self-improving agent until now turns ONE knob, edit the scaffold or train the weights. SIA turns both, in a single loop. And it rips:
LawBench: 50% → 70.1% once weights kick in
CUDA kernels: 14x speedup
RNA denoising: +502% over baseline
And the tell is on denoising—this is the part that's genuinely awesome. The scaffold-only loop, across every single iteration, never found a fix. Then the first weight update just... figured it out: a two-line clip that rounds imputed counts to non-negative integers. A basic biological constraint that no prompt, no scaffold, no amount of clever engineering ever surfaced. The model learned it on its own.
That's the whole thesis in one moment! The harness changes how an agent searches. The weights change what it knows. And you really, really want both.
Slack runs security investigations with a team of LLM agents.
Director plans. Experts gather. Critic checks the work.
25.8% of Expert findings don't clear the plausibility bar. The system catches them anyway.
https://t.co/WQa8B30RFK
Want to get better at system design? Stop reading "Top 10 Architectures" lists.
Pick one system you already use.
Figure out why it breaks.
Figure out what the team chose not to build, and why.
Constraints teach you more than diagrams ever will.
How to keep up with AI research without drowning:
- Follow 5 researchers, not 50
- Read the ablation tables, that's where the real story is
- Skip benchmarks, study methods
- One paper deeply beats ten papers skimmed
HubSpot had a 37-minute outage last month where every customer lost UI access to half their workflows.
The endpoint returned HTTP 200 the entire time.
Monitoring saw nothing wrong.
New Byte-Sized Design on why availability ≠ correctness 👇
Claude Opus 4.7 is here!
Better coding on the hardest tasks, sharper vision (3.75MP images), and a new "xhigh" effort level.
Same pricing as 4.6: $5/$25 per M tokens.
Swift just escaped Xcode.
Now runs in Cursor, VSCodium, and other AI IDEs.
Meanwhile:
• Xcode 26.4 paste bug is painful (there’s a fix)
• April 28 SDK deadline is creeping up
• A Swift game engine just dropped
Swift isn’t “just iOS” anymore.
It’s going general-purpose.
Hot take: 90% of "bad AI outputs" are a data freshness problem, not a model problem.
You're blaming GPT-4 for confidently answering with info that was accurate 3 months ago.
That's not a model failure. That's infrastructure debt.
Read more on how to solve this here!
https://t.co/IyFQgFKPHm
Hot take: most engineers gunning for Staff don't want Staff. They want to feel like their work matters.
That's a different problem. Solve that one first.
Issue #76 is out! 🚀
Swift 6.3 dropped with Android support, smarter Swift Testing, and the new attribute for C interop. Plus: Xcode 26.3's agentic coding is here, and don't miss the April 28 SDK deadline.
https://t.co/qSTe9qskVX
Swift 6.3 is out!
🎉 Highlights: official Android SDK, @c attribute for C interop, Swift Build preview in SPM, and Swift Testing improvements including image attachments & test cancellation. Cross-platform Swift is becoming very real!
https://t.co/8aaMkky9tj
📢 iOS Code Review is now open for sponsorships!
If you have a dev tool, iOS/Mac app, or product you want in front of working iOS engineers, this is your spot.
Spots are limited, get in touch soon 👇
📧 [email protected]
🔗 https://t.co/6XBbqMoc2g
Junior engineer: "How do I build this?"
Senior engineer: "Should we build this?"
Staff engineer: "Here's what happens to the team that maintains this in two years."
The question changes. That's the whole game.
The engineer who wrote the original code left 3 years ago.
The docs are wrong. The tests don't cover this path. Production is down.
This is the job. Not the algorithm interviews. This.
"We need to rewrite it."
No you don't. You need to understand it.
The rewrite will have the same problems in 18 months because the problems are in how your team thinks, not the codebase.
Hot take: every AI coding benchmark you've seen is lying to you.
Passing tests ≠ writing maintainable code.
An agent that hardcodes a brittle fix and one that writes clean extensible code score identically on SWE-bench.
SWE-CI finally measures what matters, 71 rounds of real commits, real regressions, real consequences. Most models failed 75% of the time. Only one cracked 76% clean runs.
The gap between "AI can code" and "AI can maintain code" is enormous.
Read more about this research by Ali Baba group!
https://t.co/tHxmxw2sYs