标签：#m

submitted by /u/madflojo [link] [留言]

Google Chrome version 150 and 151, expected in late June and July, respectively, will cut off support for the last remaining workarounds for running older ad blockers, 9to5Google reports. Google phased out support for ad-blocking extensions built for Manifest V2, like uBlock Origin, in 2024. At that point, most Chrome users either switched to newer […]

submitted by /u/cekrem [link] [留言]

AMD's stripping of TSME from consumer CPUs appears to be a deliberate, covert move.

Don't worry, there's yet another chatbot you can ask for restaurant recommendations.

For months, Big Tech's Washington lobbyists have chased after the holy grail of pro-AI legislation: preemption. This would be a comprehensive federal law, passed in Congress and signed by the president, applying one set of AI rules across the entire country and overriding the legally messy state-by-state approach to regulation. For months, lobbyists have run […]

Australia was the first country to issue a ban in late 2025, aiming to reduce the pressures and risks that young users may face on social media, including cyberbullying, social media addiction, and exposure to predators.

Last week, Xbox boss Asha Sharma sent a memo warning of an Xbox "reset" ahead of expected layoffs, and today, Kotaku reported that Xbox plans to shut down Compulsion Games, the studio behind South of Midnight. Since taking over in February, Sharma has made some big decisions, including cutting the price of Xbox Game Pass […]

Have you ever thought about sharing data across language boundaries without serialization? This blog post highlights the challenges behind this endeavor and how they can be overcome. Note: I'm not the original author of the blog post, but since the author does not have a Reddit account, I post it on his behalf. submitted by /u/elBoberido [link] [留言]

Roblox's vice president of safety product policy, Eliza Jacobs, told NBC News that Roblox is "optimistic" that its new facial age estimation tech will "continue to get better," saying, "Ticking a box to say you're 13 or older, it's not enough anymore." NBC invited a group of kids to try out Roblox's new video selfie […]

A blunt playbook for devs who don’t want to turn into autocomplete zombies. The first time an AI wrote code for me, I felt like I had unlocked cheat codes for real life. I typed a half-baked function name, hit enter, and suddenly I had a block of code that looked legit. It was magical. The second time, though? It suggested something so catastrophic basically the programming equivalent of pulling the fire alarm that I realized: this thing is less “mentor” and more “overconfident intern who thinks they know pointers but actually just broke prod.” That’s where most of us are right now. AI is everywhere: in our IDEs, our docs, even sneaking into PR reviews. Some days it feels like rocket fuel; other days it feels like an autocomplete with a drinking problem. The tricky part isn’t whether AI is “good” or “bad.” The tricky part is how we, as developers, use it without becoming lazy, dependent, or worse complacent. Because here’s the uncomfortable truth: AI won’t replace you, but bad AI habits absolutely will. TLDR : This article is a survival guide for developers in the AI era. We’ll break down why AI feels both magical and mid, the five switches that make AI actually useful, when to trust and when to verify, how to use AI as a research assistant (not a code monkey), the dangers of autocomplete brain, and a playbook for building a healthy workflow. Why AI feels both magical and mid Every dev I know has had that moment with AI. The first time it autocompleted a function and nailed it, you probably thought: “Wow… this thing just saved me half an hour.” It’s the same dopamine hit as discovering ctrl+r in bash or realizing you can pipe grep into less . Pure wizardry. But the honeymoon ends quickly. The same tool that wrote a clean utility function also happily hallucinates imports that don’t exist, invents APIs, and will confidently explain things that are flat-out wrong. It’s like pair programming with someone who sounds senior but has never actually shipped code. The magic-

Notes from building a memory layer that forgets on purpose. Most "memory-enabled" agents don't remember anything. They re-read. Every turn, the whole conversation gets pasted back into the prompt, and we call that memory because the model can answer questions about earlier turns. It's a good trick. I used it for months. It also falls apart the moment real people start using the thing, and it falls apart in three separate ways. The first is the one everyone notices: it's expensive and noisy. You re-send every prior turn on every request. The single line you actually care about - "I'm allergic to peanuts" - is buried under a thousand lines of small talk, and you pay for all of it, every time. The second is quieter and worse. Transcript-stuffing has no idea what stale means. If someone told your agent "I'm vegetarian" in March and "I eat fish now" in May, you've just handed the model both facts with equal weight. Now it has to guess which one is current. Sometimes it guesses wrong, and there's nothing in the system that even thinks that's a problem. The third one is the reason I stopped treating this as a side quest. When you finally add summarization to control the cost from problem one, the summarizer is free to drop whatever it wants to save tokens. Including the allergy. I spent years around fintech, where the wrong record surviving (or the right one quietly vanishing) is how people get hurt, so this landed hard: forgetting an allergy to save 40 tokens isn't a cost bug. It's a safety bug wearing a cost bug's clothes. So the question I actually wanted to answer wasn't "how do I make my agent remember more." It was: how do I build something where acting on a fact the user already retracted and silently dropping a fact that must survive are impossible by construction, not just unlikely if the prompt is good that day. Everyone has already solved one third of this The encouraging part is that you don't have to invent much. The discouraging part is that every existing sy

Most token scam detectors, including the one I work on, share one implicit assumption: the contract you analyze at launch is the contract people will trade. Read the source, simulate a buy and a sell, cluster the deployer, score it, done. That is a snapshot. And a snapshot is exactly what a patient scammer plays against. Two token designs pass every launch-time check and then turn hostile later. This is how they work, and the two on-chain techniques we shipped this week to catch them. Design 1: the delayed honeypot A honeypot is a token you can buy but cannot sell. The classic version is non-sellable from block one, so a buy-then-sell simulation catches it instantly. The patient version is sellable at launch. Early buyers sell fine, the chart looks healthy, the token earns a clean verdict from every checker that judged it at T0. Then, days later, the operator flips a switch: a timed blacklist that rejects transfers after a block height or timestamp, a setTrading(false) / pause() kill switch pulled once liquidity has accumulated, a fee setter cranked to 100% on sells. From that moment it is a honeypot. But the only verdict on record is the clean one from launch day. The detection ran once, at the worst possible time to run it. Fix: re-simulate at J7 We keep post-launch snapshots of every token at J0, J7 and J30 (originally to catch slow rugs: volume collapse, late LP burns). The new piece re-runs the full buy/sell honeypot simulation at J7, but only for tokens that were genuinely sellable at J0. A clean-to-honeypot flip is the signal: // Only for tokens sellable + tradable at J0 - a clean->honeypot flip is the point. // Bounded per run because it is RPC-heavy. const eligible = ! j0 . risk_flags . some (( f ) => J0_SKIP_RESIM_FLAGS . has ( f )); if ( rpc && eligible && resims < resimLimit ) { const isNowHoneypot = await detectLateHoneypot ( rpc , tokenAddress ); if ( isNowHoneypot ) flags . push ( " late_honeypot " ); // +40 risk at J7 } One rule we hold to: an RPC hi

A story about an AI alert model that cut false alarms from 60% to 7% — and what happened when...

A bot on Polymarket quietly extracted $32k in near risk-free profits by sniping “Will Company XYZ Beat Earnings?” markets. It waits for the official release, then instantly buys the winning side. Many limit orders from retail traders remain uncancelled, creating a post-announcement arbitrage window. Two developers decided to challenge it. Here’s what they learned while trying to build a faster version. Infrastructure Choices Location : Polymarket’s CLOB runs in AWS eu-west-2 (London). They deployed from Ireland (eu-west-1, Dublin) — the closest realistic option without IP tricks. UK IPs are blocked. Language : Rust for type safety and speed. The author notes you can achieve competitive latency in Python if you strip unnecessary network calls. Key Warning : Avoid the official Polymarket SDKs for ultra-low latency. They include helpful but slow pre-trade checks. Build lean custom clients. The Data Feed Challenge (The Real Bottleneck) The critical edge is getting earnings announcements faster than competitors. Source Performance Verdict Scraping Newswires Too slow Failed Benzinga Low-Latency Slower than manual clicking Failed Paid ultrafast feed ~500ms after release Still too slow EDGAR Consistently slower than newswires Backup only Even at 500ms, the order book was already swept by faster bots. The top players are likely using extremely expensive dedicated feeds or custom setups. Technical Lessons Learned Network > Code Most latency lives in the network round-trip, not in language choice. Optimize transport first. Custom Execution Layer Skip heavy SDK abstractions. Direct signed orders with minimal validation. Post-Event Sniping Logic Monitor newswire feeds aggressively Parse EPS vs. estimate instantly Place aggressive limit/market orders on the winning side Handle cases with ambiguity (multiple interpretations of “beat”) Reality Check They made some wins during EPS ambiguity or when faster bots hit size limits, but never won on pure speed against the leader. Why This

Honestly, bootcamp Grad Dives Into Google vs OpenAI API Pricing When I finished my coding bootcamp three months ago, I thought I understood what an API did. I mean, you send a request, you get a response back, right? What I did not understand was how dramatically the cost could vary depending on which model you picked. I had no idea that a single line of code change could mean the difference between paying pennies and paying hundreds of dollars at scale. That is the rabbit hole I fell down last week, and I want to walk you through everything I learned. This is the post I wish I had read before I burned through my first $50 in API credits. Why I Started Looking At Pricing In The First Place I was building a small app that takes user reviews and summarizes them. Pretty straightforward. I figured I would just plug in the most popular model and call it a day. That model, if you have been paying attention to the news, is GPT-4o. So I wired it up, ran a few tests, and everything looked great. Then I did the math. GPT-4o charges $2.50 per million tokens on input and $10.00 per million tokens on output. I did not even know what a "million tokens" really meant in practice. So I tested my app with maybe 50 reviews and watched my credit balance drop. It was not catastrophic, but it was enough that I started wondering if there was a cheaper way. I was shocked when I found out how big the gap actually is. The Pricing Table That Changed My Whole Plan I stumbled onto a platform called Global API, and honestly, the pricing chart there blew my mind. They give you access to 184 different AI models, with prices ranging all the way from $0.01 to $3.50 per million tokens. Compare that to the GPT-4o output price of $10.00 per million tokens, and you start to understand why I panicked a little when I saw my early numbers. Here are the five models I ended up comparing side by side: Model Input Cost Output Cost Context Window DeepSeek V4 Flash $0.27 $1.10 128K DeepSeek V4 Pro $0.55 $2.20 20

Armed with a ton of new upgrades, Ferrari came to Spain full of confidence.

EA is making a big push for more in-game advertising.

Casey Harrell has had a set of electrodes embedded in his brain for almost three years. Harrell, who has amyotrophic lateral sclerosis (ALS) and is paralyzed, first used his brain-computer interface (BCI) to “speak” sentences with the help of a research team in 2023. Since then, Harrell has clocked thousands of hours of use. He…

submitted by /u/BlondieCoder [link] [留言]