Christopher Almeida-Symons

If you use GitHub (especially if you pay for it!!) consider doing this *immediately* Settings -> Privacy -> Disallow GitHub to train their models on your code. GitHub opted *everyone* into training. No matter if you pay for the service (like I do). WTH github.com/settings/copil…

Totally had the same experience. Apple Musics interface is SO bad it really doesn't compare to the feature rich Spotify. Standards have definitely dropped since iTunes

@nicbarkeragain The original iTunes had a better interface than this. I tried moving to Apple Music from Spotify but UX you get from Spotify is SO much better it's hard to stay away from.

Usually I try to just ignore this type of stuff and get on with my day, but I recently switched from spotify to apple music for reasons™, and the desktop user interface is so comically poor that I have to make a thread highlighting what I came across in one session, strap in:

@_trish_xD HTML 😂

first time you wrote hello world - what language did you use?

Someone created a fully operational JOHNNY 5 from scratch and omg how cool 🙌 (instagram Saundersmachineworks)

@immatechy Maybe the mean The FA Cup final...oh wait

So why didn't they get to the final?

@dev_maims Because you can tell AI has got the code wrong and don't commit slop

Hot take: Vibe coding only works well if you already know how to code.

All I'm seeing from Arsena fans online is them admitting we didn't play well and Man City were the better team. Not hard to have this type of analysis. Not the result we wanted but we move on #COYG

In the past 3 years, I haven't noticed any uptick in release speed for software I use. If productivity is increasing, I can't tell as an end user. I have noticed decreases in uptime, increases in bugs, and a HUGE increase in people bragging about how many PRs per day they land.

@redtachyon @getifyX Exactly. I've tried AI in difficult situations and it got it all wrong. Then the answer was right in the docs

BTW if you agree with this, you don't work on difficult enough problens

Hmmm interesting

@ConsoomerLs The use of the words "work family" says it all

HR expects you to take 5-10 minutes for lunch breaks.

🚨BREAKING: OpenAI published a paper proving that ChatGPT will always make things up. Not sometimes. Not until the next update. Always. They proved it with math. Even with perfect training data and unlimited computing power, AI models will still confidently tell you things that are completely false. This isn't a bug they're working on. It's baked into how these systems work at a fundamental level. And their own numbers are brutal. OpenAI's o1 reasoning model hallucinates 16% of the time. Their newer o3 model? 33%. Their newest o4-mini? 48%. Nearly half of what their most recent model tells you could be fabricated. The "smarter" models are actually getting worse at telling the truth. Here's why it can't be fixed. Language models work by predicting the next word based on probability. When they hit something uncertain, they don't pause. They don't flag it. They guess. And they guess with complete confidence, because that's exactly what they were trained to do. The researchers looked at the 10 biggest AI benchmarks used to measure how good these models are. 9 out of 10 give the same score for saying "I don't know" as for giving a completely wrong answer: zero points. The entire testing system literally punishes honesty and rewards guessing. So the AI learned the optimal strategy: always guess. Never admit uncertainty. Sound confident even when you're making it up. OpenAI's proposed fix? Have ChatGPT say "I don't know" when it's unsure. Their own math shows this would mean roughly 30% of your questions get no answer. Imagine asking ChatGPT something three times out of ten and getting "I'm not confident enough to respond." Users would leave overnight. So the fix exists, but it would kill the product. This isn't just OpenAI's problem. DeepMind and Tsinghua University independently reached the same conclusion. Three of the world's top AI labs, working separately, all agree: this is permanent. Every time ChatGPT gives you an answer, ask yourself: is this real, or is it just a confident guess?

🚨 Stanford researchers just exposed a weird side effect of AI that almost nobody is talking about. The paper is called “Artificial Hivemind.” And the core finding is unsettling. As language models get better, they also start sounding more and more the same. Not just within a single model. Across different models. Researchers built a dataset called INFINITY-CHAT with 26,000 real open-ended questions things like creative writing, brainstorming, opinions, and advice. Questions where there isn’t a single correct answer. In theory, these prompts should produce huge diversity. But the opposite happened. Two patterns showed up: 1) Intra-model repetition The same model keeps producing very similar answers across runs. 2) Inter-model homogeneity Completely different models generate strikingly similar responses. In other words: Instead of thousands of unique perspectives… We’re getting the same few ideas recycled over and over. The authors call this the “Artificial Hivemind.” It happens because most frontier models are trained on similar data, optimized with similar reward models, and aligned using similar human feedback. So even when you ask something open-ended like: • “Write a poem about time” • “Suggest creative startup ideas” • “Give life advice” Many models converge toward the same phrasing, metaphors, and reasoning patterns. The scary implication isn’t about AI quality. It’s about culture. If billions of people rely on the same systems for ideas, writing, brainstorming, and thinking… AI might slowly compress the diversity of human thought. Not because it’s trying to. But because the models themselves are drifting toward the same answers. That’s the real risk the paper highlights. Not that AI becomes smarter than humans. But that everyone starts thinking like the same machine.

Arsenal averaged 31.4 seconds per restart vs Brighton. #BHAFC average 26.5 seconds per restart this season but averaged 38.7 vs Man City (H), 33.7 vs Chelsea (A) & 33.2 vs Brentford (A). Common dominator? They won all three matches. Context is key. ⬇️ football.london/arsenal-fc/new…

JavaScript's date object has been tricky for years, but that is changing. The NEW Temporal API brings, 🌍 Easily handle time zones 📆 Precise date math 🕒 Parse ISO strings without errors ⌛ Durations, date ranges, and more. Start experimenting 👇 developer.mozilla.org/en-US/docs/Web…

You now get a free kick for running into someone and being too weak. Good to know

@ColinSpreag Don’t you start haha

From this point, anything less than the quadruple from Arsenal would be abject failure.

"Howdy Folks, I'm Michael Pyrcz, a professor at The University of Texas at Austin, and I record all of my lectures and put them on YouTube so anyone can follow along!" ...and I kept doing that, and writing a Python package, along with 2 free, online e-books, 100s of Python demonstration workflows, dozens of synthetic datasets, etc. etc. Why? So anyone can follow along! Education changes lives. I know because it changed mine. I’m just paying it forward.

'It’s false to say Arsenal are reliant on set-piece goals' – @Dani77k justifies with data #SportsZone | #JoySports