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This is a follow-up to my earlier post, " Automating a config migration with a one-shot launchd job ." Some breaking changes come with a known expiration date, and you can prepare for them long before they land. This time the external event was the end-of-life of Fable 5 (2026-07-07), and I'll walk through how I designed a launchd job you set up today, that fires only on the target day, and that removes itself once it's done. The whole thing started with the thought, "manually fixing this on the shutdown day is going to be annoying." But I also didn't want to run a script every morning that needlessly rewrites JSON. What I landed on was a three-part set: a date gate, a jq rewrite with a backup, and self-unload. The problem: on the day I learn about a deprecation, I want to plant a job that "only runs on the target day" Right now, ~/.claude/settings.json looks like this: { "model" : "claude-fable-5[1m]" , ... } The moment I learned Fable 5 would end on 2026-07-07, creating a calendar reminder to manually rewrite this "model" felt too flimsy — I'll forget. On the other hand, making "a daemon that checks the date every time it boots" is overkill. What I wanted was a job I could set once and leave alone, that runs when the day arrives, and then disappears. launchd can fire at a specified time via StartCalendarInterval . But you can't express "just once at 9:00 on 7/7"; you need a combination of recurring and date-fixed slots. Specifying multiple slots and absorbing the redundancy with idempotency is the standard trick on macOS launchd. The implementation: the three-part set Here's the full ~/.claude/scripts/model-transition-0707.sh (comments omitted). #!/bin/bash set -uo pipefail SETTINGS = " $HOME /.claude/settings.json" LOG = " $HOME /.claude/logs/model-transition.log" PLIST = " $HOME /Library/LaunchAgents/com.shun.model-transition-0707.plist" log () { echo "[ $( date '+%F %T' ) ] $* " >> " $LOG " ; } # ① 日付ゲート if [ " $( date +%Y%m%d ) " -lt 20260707 ] ; then log "ski

Meta has deactivated the Muse Image capability to create AI deepfakes of any public Instagram account you @-mention.

Johannes Heidecke’s departure comes as OpenAI tries to further integrate its research and safety teams.

CISA said it "missed" an opportunity to get ahead of the security incident by not creating a response plan ahead of time.

submitted by /u/therafort [link] [留言]

Offloading fan-out work to Render Workflows (docs linked above). Retries and parallel tasks live there. My problem is the web layer. First version: return 202, tasks POST back to /internal/events with a bearer token, UI subscribes over SSE. Added a reconciler that polls the Render API every 2s anyway because I didn't trust callbacks alone. Second version: skip callbacks entirely. One POST stays open, poll getTaskRun every 1.5s in an async generator, stream SSE until the digest finishes. Postgres at the end. Less wiring, but the HTTP request lives for the whole run. Both work on small traffic. I'm not sure which one I'd keep if this wasn't a demo. Restarting the API wipes in-memory viewer state in the callback version. Workflow keeps going, which is fine, but the UI looks stuck unless you reconcile. Polling version doesn't have that split because the request IS the session. Has anyone shipped callbacks + poll backup long term? Or do you pick one and accept the downsides? Callback handler: github.com/ojusave/dealhealth-playground/blob/main/services/api/src/routes/events.ts Poll loop: github.com/ojusave/read-it-for-me/blob/main/server/lib/orchestrator.ts submitted by /u/ojus_render [link] [留言]

The status of the controller As of March 2026, the Kubernetes SIG Network stopped maintaining ingress-nginx. That is the controller a lot of clusters have been running for years. A CNCF blog post published July 9 walks operators through the state of play. The headline for anyone still on it is short: unpatched CVEs, and no more feature work. The post names two operational risks explicitly. New security issues will not receive upstream fixes. Feature updates and community support have stopped. If your ingress plane is a piece of infrastructure you have not touched in a while, this is the reason to pull it up in this quarter's planning doc. What it means at 3am An ingress controller sits between the internet and your services. When it drops a request, you find out from your users. When it takes a CVE and no one is patching, you find out from a scanner or from a report. Neither is a good discovery path. The controller also carries the exact set of annotations, TLS defaults and rewrite rules your workloads rely on. Nothing about a retirement changes the version you have in production today, so the immediate blast radius is zero. The risk is on the calendar, not on the pager. That is the kind of risk teams reliably defer until a scanner flags an unpatched CVE. The two paths CNCF lays out The post frames the choice as a fork. Path A is a lateral swap to another Ingress controller. The example named is Contour, described in the post as Envoy-based. This keeps you on the Ingress API and mostly moves the problem of who is patching. Path B is modernization to the Gateway API, described in the post as the upstream-backed successor to Ingress. The CNCF post points at ingress2gateway to automate the translation, and recommends an incremental rollout: run the new plane in parallel and move non-critical workloads first. The stopgap version is a mix. Adopt Contour to buy time on maintained code, then schedule the Gateway API move on your own calendar rather than under duress. What

My GPT got dumber after the update" gets blamed on the model regressing, or on you prompting worse. Both are unfalsifiable, and both send you to fix the wrong layer. The layer that actually moved is the one you can pin. "The model" is two layers. The weights — the trained network, slow to change, and when they do change it's announced under a new name. And the wrapper — the router that picks which model answers, the system prompt, the default reasoning effort, verbosity caps. The wrapper changes silently, on its own schedule, per product. It's almost always what moved under you. So stop re-tuning prompts to chase it. Pin the wrapper: Force the route. Don't leave it on Auto — set Thinking (or say "think hard") so the router can't quietly demote your prompt to a faster, weaker model. OpenAI's own GPT-5 launch post describes exactly this router (it scores prompts "simple" vs hard); after the backlash they put the picker back (Auto/Fast/Thinking — TechCrunch, Aug 2025). Pin the version. If you build on a model, call its exact versioned ID via the API. A model ID's weights don't change — new versions ship under new IDs — so router and system-prompt churn can't reach you. Own the harness. Running agents? Set the system prompt, reasoning effort, and verbosity yourself instead of inheriting a default. Anthropic's own April 23 post-mortem is the proof: six weeks of "Claude Code got worse" traced to three wrapper changes (a reasoning-effort downgrade, a reasoning-history bug, a verbosity cap their ablations put at ~3% quality) — API weights never touched. A real weights change — a new model — will still move behavior. But that's announced, and you choose when to adopt it. The silent stuff is all wrapper, and the wrapper is the part you can pin. Sources: OpenAI GPT-5 launch (router + "think hard"); TechCrunch, Aug 2025 (model picker reinstated); Anthropic April 23 post-mortem (anthropic.com/engineering/april-23-postmortem); InfoQ and VentureBeat (corroboration); Claude platfor

The first year class HelloWorld { public static void main ( String args []) { // Displays "Hello World!" on the console. System . out . println ( "Hello World!" ); } } The second year /** * Hello world class * * Used to display the phrase "Hello World" in a console. * * @author Sean */ class HelloWorld { /** * The phrase to display in the console */ public static final string PHRASE = "Hello World!" ; /** * Main method * * @param args Command line arguments * @return void */ public static void main ( String args []) { // Display our phrase in the console. System . out . println ( PHRASE ); } } The third year /** * Hello world class * * Used to display the phrase "Hello World" in a console. * * @author Sean * @license LGPL * @version 1.2 * @see System.out.println * @see README * @todo Create factory methods * @link https://github.com/sean/helloworld */ class HelloWorld { /** * The default phrase to display in the console */ public static final string PHRASE = "Hello World!" ; /** * The phrase to display in the console */ private string hello_world = null ; /** * Constructor * * @param hw The phrase to display in the console */ public HelloWorld ( string hw ) { hello_world = hw ; } /** * Display the phrase "Hello World!" in a console * * @return void */ public void sayPhrase () { // Display our phrase in the console. System . out . println ( hello_world ); } /** * Main method * * @param args Command line arguments * @return void */ public static void main ( String args []) { HelloWorld hw = new HelloWorld ( PHRASE ); try { hw . sayPhrase (); } catch ( Exception e ) { // Do nothing! } } } The fifth year /** * Enterprise Hello World class v2.2 * * Provides an enterprise ready, scalable buisness solution * for display the phrase "Hello World!" in a console. * * IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED * TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER * PARTY WHO MAY MODIFY AND/OR REDISTRIVUTE THE LIBRARY AS * PERMITTED ABOVE, BE LIABLE TO YOU FOR DAM

Pull a hot steel bolt out of a furnace and quench it in oil, and a fair question is: does the bolt cool from the outside in, with a sharp temperature difference between its skin and its core, or does the whole thing drop in temperature more or less together? The answer is not obvious from the part itself. A thin copper washer and a thick ceramic block behave very differently in the same bath, even at the same starting temperature. The Biot number is the small calculation that settles this question before you commit to any heavy analysis. It tells you, in a single dimensionless figure, whether an object can be treated as having one uniform temperature or whether you must resolve a temperature gradient inside it. That distinction changes the math from a one-line exponential decay to a partial differential equation. Why this calculation matters Transient heating and cooling problems show up everywhere: heat-treating metal parts, quenching forgings, cooling electronics, baking or chilling food, warming up an engine block. In every one of these, the engineer wants to know how the temperature changes over time. The hard version of that question requires solving the heat conduction equation across the body, with position and time as variables. The easy version is the lumped-capacitance model, which treats the whole object as a single point at one temperature. It reduces the problem to a simple first-order exponential. The catch is that the lumped model is only valid when internal conduction is fast compared with surface convection. The Biot number is exactly the check that tells you whether that condition holds. Skip the check and apply the lumped model where it does not belong, and you can badly mispredict cooling times, residual stresses, and the risk of cracking from thermal gradients. The core formula The Biot number compares two thermal resistances. One is the resistance to conducting heat through the inside of the solid. The other is the resistance to carrying heat a

n8n is the open-source workflow automation tool that competes with Zapier and Make. I have been running it for saas.pet's content pipeline for 6 months. Here is my honest take on self-hosting n8n versus paying Zapier, and whether it is worth the hassle. What n8n does that Zapier cannot n8n is an open-source workflow automation platform with 400+ built-in integrations. You connect nodes on a visual canvas: when X happens in one app, do Y in another. The killer difference from Zapier: you control where it runs. Self-host on a $5/month VPS or run on n8n Cloud at $20/month. No per-task pricing, no 'you hit your zap limit' emails. For high-volume workflows, the cost difference is dramatic. I run n8n on the same $6/month HK server that hosts my proxy. 12 workflows handle saas.pet's entire content pipeline: daily data fetch from GitHub Trending API, transform JSON, write to data files, trigger build, push to git, notify me on Telegram. The same workflows on Zapier would cost $73.50/month (Professional plan with 2,000 tasks). On n8n, $6/month for the server plus $0 for the software. The self-hosting overhead is real—updates, SSL certs, monitoring—but for 12+ active workflows, the savings are $800+/year. If you only have 2-3 simple zaps, stay on Zapier's free tier. If you have 5+ workflows with volume, n8n pays for the hosting in month 1. My 6-month setup for saas.pet I run n8n in Docker on the HK server. The initial setup: install Docker, pull n8n image, configure nginx reverse proxy, set up SSL via Certbot. That took about 2 hours the first time. Now I can deploy n8n in 15 minutes on a fresh server. The 12 workflows: (1) Daily GitHub trending fetch via saas.pet/api/trending, (2) data transform to unified JSON, (3) write to data/YYYY-MM-DD.json, (4) trigger build-ci.mjs, (5) git add + commit + push, (6) Telegram notification with commit SHA, (7) weekly sitemap health check, (8) monthly backup of reviews/ JSONs to S3, (9) uptime ping every 15 minutes, (10) DNS health check,

Meta told Dylan Byers, of Puck News, that it had nixed the feature after backlash from its user base.

Following significant backlash, Meta is turning off the feature it announced this week that let users generate AI images based on content from public Instagram accounts just by tagging them. The feature, as originally set up, meant that content from any public Instagram account could be used in AI creations without the account owner's permission. […]

Data centers are driving up the company’s use of electricity—and carbon pollution.

Reinforcement learning uses error information to adjust control algorithms.

The café was crowded that evening, but to me, the world had fallen completely silent. An open...

submitted by /u/PinkFrojd [link] [留言]

The incident that taught me to respect pinning looked like nothing. A service freshly migrated to virtual threads, a load test that plateaued at about 420 requests per second no matter how much traffic we threw at it, CPU sitting at 9%, zero errors, zero warnings, nothing in the logs. The machine had 8 cores, and the one downstream HTTP call in the hot path took about 19 ms. Do the arithmetic: 8 × (1000 / 19) ≈ 421. The service that was supposed to scale to millions of virtual threads was serving exactly one request per CPU core. Loom had quietly handed us back a bounded thread pool, and the code looked perfectly innocent. That failure mode has a name — pinning — and this is the field guide I wish I'd had that night: what it is, the two (and only two) things that cause it, what JDK 24 changed, and how to catch it before your throughput graph does. What pinning actually is A virtual thread doesn't own an OS thread. It runs on a small pool of platform threads called carrier threads — concretely, the workers of a dedicated ForkJoinPool living in a thread group named CarrierThreads , with default parallelism equal to Runtime.availableProcessors() . When a virtual thread blocks — on I/O, a lock, a queue — it normally unmounts : it saves its stack, steps off the carrier, and frees that carrier to run another virtual thread. That unmount is the entire trick that lets a handful of OS threads serve millions of virtual ones. Pinning is when the unmount can't happen. The virtual thread blocks but stays mounted, and its carrier sits there doing nothing useful for the whole duration. One pinned carrier is a rounding error. But the default carrier pool is only as big as your core count, so if a hot path pins routinely, you pin every carrier at once — and then no virtual thread anywhere makes progress. That's not a slowdown; it's scheduler starvation, and from the outside it looks a lot like a deadlock. You can raise the ceiling with -Djdk.virtualThreadScheduler.parallelism=N , bu

Schneider, who formerly served as the CEO of Automattic and is a partner at True Ventures, says he is "all in" on the unconventional social media platform.

When building an AI agent, you need LLM calls. It can be done via a remote API or a local API, but either way you need to do it. Whether the agent is a simple conversational agent or a ReAct agent with a bunch of tools, whether it's using a complex graph or a simple RAG, it must be based on the concept of sending prompts to the language model. But, what if we replace the language model with... another agent? Let's say we already have a smart agent with a bunch of tools that can handle complex problems. Why not use it to build a new agent on top of it? This way we can focus on the specific custom functionality we want to achieve, while already having the common functionalities covered by the underlying agent. You might think this must be expensive. You get a better performance, so you have to pay for it, right? Well, not necessarily. The catch is that the coding assistants are actually surprisingly cheap when compared to API prices. They offer much more than raw LLM calls, they offer amazing agent functionality, but the cost is actually lower, and it's not a small difference. The cost of LLM calls per 1M tokens is usually between $2 and $7. For coding assistant subscriptions, it's a bit more tricky to calculate because you pay for monthly subscription, but it can be still converted to per 1M tokens cost, and from what LLM just told me it is around $0.08 to $2. That's a huge difference! And the complex agents are cheaper than raw LLM's! according to ChatGPT: Service Cost per 1M tokens Codex / ChatGPT coding plan ~$0.08 Cursor Pro ~$0.08–0.25 GitHub Copilot Pro ~$0.10–0.30 Claude Pro / Claude Code ~$0.74 GPT-5 API ~$2.1 Claude Sonnet API ~$4.2 Claude Opus API ~$7.0 according to Claude: Service Cost per 1M tokens Haiku 4.5 (API) $1.80 Sonnet 5 (API, intro thru Aug 31 '26) $3.60 Sonnet 5 (API, standard) $5.40 Opus 4.8 (API) $9.00 Fable 5 (API) $18.00 Claude Code — Pro ~$1.10–$2.15 (est.) Claude Code — Max 5x ~$2.10–$4.15 (est.) Claude Code — Max 20x ~$2–$4 (est.) So, why