I pin model IDs on purpose. Floating aliases have burned me before — a silent swap under a -latest tag once changed a tool-calling detail in production and cost me a Saturday with git bisect and a coffee I didn't enjoy. So everything I run points at a dated snapshot. gpt-5-codex was one of them. Last week I finally read OpenAI's deprecations page top to bottom instead of skimming it, and there it was: gpt-5-codex , retiring 2026-07-23 , sitting quietly next to ten of its neighbors. As I write this it's July 16. That's seven days . If you pinned any of the snapshots below, consider this your heads-up. Here's the full retirement list, how to find out in about ten minutes whether you're exposed, the replacement map with the things I'd actually regression-test, and the one structural change that turned my last forced migration from scary into boring. The shutdown list (retiring 2026-07-23) Straight from OpenAI's API deprecations page . I checked each row against the source while writing this — do the same before you act on it, because dates and replacements do get revised. Retiring model OpenAI's suggested replacement gpt-5-codex gpt-5.5 gpt-5.1-codex gpt-5.5 gpt-5.1-codex-max gpt-5.5 gpt-5.1-codex-mini gpt-5.4-mini gpt-5.2-codex gpt-5.5 gpt-5-chat-latest gpt-5.5 gpt-5.1-chat-latest gpt-5.5 gpt-4o-search-preview-2025-03-11 gpt-5.4-mini gpt-4o-mini-search-preview-2025-03-11 gpt-5.4-mini gpt-4o-mini-tts-2025-03-20 gpt-4o-mini-tts-2025-12-15 computer-use-preview-2025-03-11 computer-use-preview (or gpt-5.4-mini ) Note the shape of it: the *-codex line collapses into gpt-5.5 — except gpt-5.1-codex-mini , which drops to gpt-5.4-mini — the chat-latest aliases fold into gpt-5.5 too, the two *-search-preview snapshots move to gpt-5.4-mini , and the two preview families ( tts , computer-use ) just roll to a newer dated snapshot / the undated alias. Once you see the buckets, the migration is less intimidating than the eleven-row table looks. Find out if you're exposed (about ten m

How to Automate SEO Content Publishing Without Breaking Your Workflow Managing SEO content at scale is one of those problems that looks simple until you're staring at a spreadsheet of 200 articles in various stages of draft, review, and scheduled publication — and you still have to manually paste metadata into WordPress, set canonical tags, and remember which pieces need internal links updated. Automating SEO content publishing means connecting your content pipeline — from keyword targeting through final scheduling — into a repeatable system where the manual handoffs disappear. The short version: you use a combination of a CMS with robust API access, a content workflow tool or spreadsheet-to-publish bridge (like Zapier, Make, or a custom script), and structured content templates with pre-filled SEO fields, so that a piece of content moves from approved draft to live URL without someone doing ten small tasks by hand. The rest of this tutorial is about how that actually works, where it breaks, and what's not worth automating. What You Actually Need Before You Start Automating Most guides jump straight to tools. That skips the part that determines whether automation saves you time or just makes your errors faster. Before any automation runs, your content process needs to be defined well enough to describe in writing. Can you list every step from "keyword approved" to "post is live" right now, including who does what? If that list doesn't exist yet, building automation on top of undefined process is how you end up with 40 posts published with missing meta descriptions and no one knowing why. The other thing people underestimate: your CMS needs to support programmatic publishing. WordPress with REST API enabled, Webflow's CMS API, Contentful, Ghost — these all work. A legacy CMS that requires someone to log in and click publish is a wall, not a speed bump. If your platform doesn't have an API or a native integration path, you're looking at a rebuild before automation is

The Future of Rust: Why This Memory-Safe Language is Dominating Systems Programming in 2026 In its early years, Rust was often viewed as a "rising star"—a promising language with significant potential but a steep learning curve. As we navigate through 2026, that narrative has fundamentally shifted. Rust has transitioned from a niche interest to a cornerstone of modern, memory-safe infrastructure. The language is no longer just proving its worth; it is defining the gold standards for systems programming, cloud-native backend services, and kernel development. This deep dive explores the key pillars driving Rust's evolution, its massive ecosystem growth, and the roadmap for the years ahead. Seamless Ownership: The New Era of Rust Ergonomics One of the most significant shifts in recent Rust development has been the relentless focus on language ergonomics. Historically, developers occasionally felt that custom smart pointers were "second-class citizens" compared to built-in references. The "Beyond the & " initiative has successfully bridged this gap. Through advancements in Smart Pointer Parity , developers can now use custom pointers—such as Rc , Arc , or specialized interop pointers—with the same intuitive syntax and capabilities as standard references. Furthermore, the introduction of sophisticated field projection mechanisms and &own references allows for unprecedented precision in managing ownership and borrowing. This makes complex data structures much easier to implement and reason about, drastically reducing the friction typically associated with high-level abstractions. The Async Revolution: Making Asynchronous Code Natural Rust has achieved a milestone often referred to as "Async Parity," aiming to make asynchronous programming feel as natural and seamless as synchronous code. Several key developments have fueled this revolution: Async-in-Traits: The stabilization of async fn in traits has removed the need for external crates like async-trait , greatly simplify

Every GEO ("generative engine optimization") tool, including ours until recently, sells some version of the same pitch: fix your robots.txt, add Schema.org markup, write FAQ schema, and AI engines will cite you more. We build one of these tools — Causabi scans sites for AI-crawler readiness and generates fix files (robots.txt, llms.txt, JSON-LD, FAQ blocks). As part of validating our own scoring weights, we ran the numbers on whether the score actually predicts getting cited. Short version: it mostly doesn't, once brand prominence is in the picture. What we measured We scored 44 domains on a 6-category on-site readiness algorithm: robots.txt (AI bots allowed or blocked) Schema.org (Organization/LocalBusiness JSON-LD completeness) FAQ schema (FAQPage markup, 3+ entries) content depth/structure brand/NAP signals freshness (dateModified, recency) Then we checked how often each domain actually got cited by an AI engine (Claude, via its web-search tool, one measurement window, a fixed prompt set per domain). What we found On-site score vs. citation rate: Pearson r ≈ -0.08, Spearman ρ ≈ -0.03. Functionally no correlation — if anything, a very slight negative one, which is more likely noise than a real inverse relationship at this sample size. 86% of the 44 domains got zero citations in the window, independent of their score. The domains that did get cited clustered almost entirely by brand prominence — well-known domains got cited at a noticeably higher rate (~0.16 of prompts) than everyone else (~0 for the rest of the sample), regardless of how well-optimized their markup was. Why I'm not overselling this n=44 is small. This is an internal validation exercise for our own product, not a peer-reviewed study, and I don't want it read as one. Specific caveats: Single engine (Claude) this round. Citation behavior differs meaningfully across ChatGPT, Gemini, Grok, and Perplexity — we haven't run the same check across all four yet. One time window, no longitudinal before/after.

#NebiusServerlessChallenge Live dashboard: Myth-Busting Quantitative Terminal · Live endpoint: /predict on Nebius Serverless · Code: github.com/KoralZakai/stocksPredictionAfterTweet Everyone on a trading desk knows the story. He tweeted about Intel, and the stock ran for months. He posts about Iran, oil spikes. The anecdotes are vivid, specific, and everybody has one. While the prevailing myth suggests that Trump's tweets drive market movements, my research — built on an analysis of 78,130 posts, with Llama-3.3-70B reading every market-relevant tweet — reveals a more nuanced reality. The findings demonstrate that the market tends to price in significant events long before the tweet is even posted. This indicates a case of reverse causality, where the market dictates Trump's narrative, rather than the other way around. He isn't moving the market; he is simply riding the wave of established trends. Forward — the direction you could trade — the signal is a coin flip, and I can show that with pre-registered tests rather than vibes: 0 of 63 cells survive correction. Getting to that answer honestly was the hard part. Seven times this pipeline produced a beautiful, publishable, completely false positive — each one looking exactly like the discovery the anecdotes promise. Those seven are the engineering content of this post. The setup Data. 78,130 public posts; 8,317 in the study window (2025-01-01 → 2026-07-06). Daily OHLCV bars for 62 tickers, committed to the repo. Public data only — a stranger can re-run every number without a single private key. The model. meta-llama/Llama-3.3-70B-Instruct on Nebius AI Studio , zero-shot, reading only the tweet text : what is this post about, which instruments does it touch, and which way does each one go. 476 tweets, 1,296 instrument calls. No fine-tuning — the question is whether the text carries signal, and a fine-tune would smuggle the outcome into the answer. The architecture. Nebius Serverless AI Jobs run the batch research pipel

Yes, it was, in fact, a Unix system.

Disclosure: this is a write-up about my own side project — a combat analytics tool for AION 2. No affiliation with the game's publisher. Links at the end. Architecture A Windows desktop app: Rust backend + Tauri v2 webview UI . It passively captures the game's TCP traffic (npcap), reassembles streams, parses the game's undocumented binary protocol, feeds a combat model (damage, heals, buffs, deaths, boss detection), and pushes aggregates to a small JS frontend. Nothing touches the game client — no injection, no memory reading. If the packet didn't say it, we don't know it. Pain #1: the protocol is a moving target Nobody hands you a spec. The protocol is varint-heavy, partially compressed, and changes with game patches. You end up doing packet archaeology: capture a fight, stare at hex dumps, correlate "I pressed this skill at 19:32:04" with byte patterns, build a parser, and then — the fun part — keep it alive after every patch , usually reverse-engineering the diff within hours because your users' raids are tonight, not next week. One hard-won lesson: log everything behind toggleable trace categories. Our tracing setup keeps hot-path log callsites at literally zero cost when disabled (Rust tracing with Interest::never() + atomic per-category flags), so a user can flip a "Trace: Packets" checkbox, reproduce a bug, and send a log that actually contains the bytes we need. Pain #2: entity identity is a lie The single hardest correctness problem wasn't parsing — it was identity . A player is not one ID: your character has a stable "owner" entity that carries your buff bar, your damage lands under a transient combat entity whose ID changes between pulls, leave the dungeon and the game re-binds you to a brand-new ID, names arrive from different packets than damage, sometimes seconds later, sometimes never (mid-fight app start). Get any of this wrong and a healer's healing lands on a ghost row, or a player's buffs vanish from the saved fight because their ID was recycled 7

This is the entire Firefox browser rendering to a <canvas> element. Gecko, all UI components, and the Spidermonkey JS engine are all compiled and running in WebAssembly. Here are a few things you might find interesting: - This is fully end to end encrypted! We use the WISP protocol for TCP-over-websockets. - There is a novel WASM->JS JIT for experimental site speedup - This port cost over 25k in opus/fable tokens for debugging and JIT research This was just a fun experiment to push the boundarie

The Trump administration has not disclosed its cost estimates for the Iran war.

Since WIRED reported on Meta’s NameTag face recognition system, company executives have made confusing and conflicting remarks about its very existence.

Hey HN! I've always loved beautiful words. I also enjoy coming up with fun names for all the side projects and businesses that I'm working on, trying to find something unique with an available .com. After spending countless hours brainstorming names for different stuff, I built Spellsurf. It lets you explore words and combine their syllables to create new ones. You can also set rules for the generated words, making it easy to search for names that fit what you're looking for. People say to open

The safety board confirmed Tesla's account of the crash, which the company shared days after it happened last month.