Claude Opus 5.5

GPT-6 Sol and Luna

OpenAI GPT–6 Astra breaks Enigma message that has resisted solution since 2005

<a href="https:&#x2F;&#x2F;archive.ph&#x2F;0V37g" rel="nofollow">https:&#x2F;&#x2F;archive.ph&#x2F;0V37g</a>

Pentagon says overreliance on AI contributed to missile strike on Iran school

<a href="https:&#x2F;&#x2F;archive.ph&#x2F;96YBP" rel="nofollow">https:&#x2F;&#x2F;archive.ph&#x2F;96YBP</a>

'We hacked the FBI:' Hackers say they have data on all FBI employees

How did AMD Ryzen get 50% faster in two years?

Visual FoxPro stopped at version 9 in 2007. A surprising amount of it is still running, in 32 bits, because rewriting a 20-year-old business app is how you lose the business. A customer wanted to keep milking their app for the foreseeable future, so here it is: the same language on a new runtime (Rust, compiled to wasm, checked against the real vfp9.exe), tables no longer stopped at 2 GB, the old 32-bit .fll add-ins still loading, and lambdas, JSON and an HTTP server bolted on for good measure. Reports are not done and the builds are unsigned. MIT because why not?

Microsoft killed FoxPro in 2007. Anyway, here's FoxPro revived

OpenAI is well positioned to fast-follow Jev

Claude Opus 5.5 Intelligence, Performance and Price Analysis (Max)

SAML: A fractal of bad design

What California is learning from solar panels built over irrigation canals

WordPress: Unauthenticated path traversal leading to conditional RCE

<a href="https:&#x2F;&#x2F;github.com&#x2F;unreallabsai&#x2F;unreal-agent" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;unreallabsai&#x2F;unreal-agent</a>

Unreal Agent

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People hooked on vapes try a new way to quit: cigarettes

ReBarUEFI: Resizable BAR for almost any UEFI system

Markdown in /src

Obscura: VPN that can't log your activity

Grammarly will send unhinged messages to all your users if you try to cancel

Transit rewards

Hi HN! I built JevBench because Jev kicks ass, and the world deserves to know how the serious open source and fake lookalike projects really perform in comparison.Jev-class models return bounded choices and probabilities instead of text, and are disruptively faster and cheaper than LLMs, while being similarly intelligent on the text input they operate on.JevBench allows looking at accuracy, latency and price all at once, in a weighted way - you can even configure the weighting.A full run asks 534 English decisions. The v1.3 score combines chance-corrected Intelligence, Calibration, Speed and Cost.Leaderboard right now:<pre><code> #1 - Jev 74.4
 #2 - SemIf 73.1
 #3 - djev 73.0
 #4 - Winnow-12B Q8 71.2
 #5 reflex 4B 70.3.
</code></pre>
MIT harness, public items, frozen artifacts, scoring code and public per-task outcomes:<a href="https:&#x2F;&#x2F;github.com&#x2F;fstandhartinger&#x2F;jevbench" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;fstandhartinger&#x2F;jevbench</a>Two no-signup demos:<a href="https:&#x2F;&#x2F;who-is-right.app.mintapis.com" rel="nofollow">https:&#x2F;&#x2F;who-is-right.app.mintapis.com</a><a href="https:&#x2F;&#x2F;is-it-ai-slop.app.mintapis.com" rel="nofollow">https:&#x2F;&#x2F;is-it-ai-slop.app.mintapis.com</a>Limitations: English-only; latency from one German server; local&#x2F;demo latency gets a disclosed ×2 adjustment (+150 ms on my servers) which is an informed assumption; held-out prompts still reach evaluated services; ~1-point gaps can be noise.Wdyt?