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.
More police and firefighters use drones to catch and deter illegal fireworks.
Last year, we told you about Xtra, the company that lets DJI sneak its popular cameras into the US, and Skyrover, a brand seemingly selling DJI drones in disguise. They're just two of the many firms DJI is suspected of starting to skirt the United States' foreign drone ban. But it appears the FCC is […]
The café was crowded that evening, but to me, the world had fallen completely silent. An open...
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
There's a moment in every Solana project where the work stops being about whether the program works and starts being about whether it's ready . You've tested it, the logic holds, the constraints are tight. Then you point it at mainnet, and a different set of questions shows up: questions about money, permanence, and strangers. This post is about that transition. Not the commands, which are short and well documented, but the shift in what you're responsible for once real users can touch your code. If you've been building on devnet and you're starting to think about a live launch, this is the mental model to carry in. Devnet was a sandbox. Mainnet is not. Devnet is a practice field. The SOL is free, you airdrop more whenever you run low, and if you deploy something broken, the only casualty is your afternoon. That safety is the whole point of devnet: it lets you fail cheaply and often, which is exactly how you should be learning. Mainnet removes the safety net, and three things change the moment you cross over. The SOL is real. Deploying a program allocates an on-chain account sized to your compiled binary, and you pay rent for that space in actual SOL. Larger programs cost more. This isn't a huge sum for a typical program, but it's real money leaving a real wallet, and that alone tends to sharpen how carefully you check things before you hit deploy. The audience is real. On devnet the only person calling your program is you. On mainnet, anyone can find your program and send it any transaction they like, the moment it's live. Everything from the security arc stops being theoretical: the accounts strangers pass in, the inputs you didn't expect, the edge cases you hoped no one would hit. Mainnet is where "every account is attacker-controlled until proven otherwise" becomes a live condition rather than a lesson. The mistakes are visible. A bad devnet deploy disappears into the noise. A bad mainnet deploy is a public event, on a permanent ledger, in front of the users you