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Lionel Messi and Cristiano Ronaldo are betting on AI, health tech, and startups. Mohamed Salah is taking a more traditional route beyond football.
Like its U.S. counterpart, the European Chips Act aims to foster the semiconductor industry — in part thanks to state subsidies. One of the beneficiaries is QuantumDiamonds, a German startup that applies a novel approach to inspecting chips.
After more than a decade of pushback, farmers and repair advocates have won access to equipment and services John Deere had long kept under its control.
Experiments in using AI to build AI show that the future doesn’t just belong to the frontier labs.
Watch the fires burning on the far shore. Don't cross until they've burned themselves out. — The 36...
Have you ever wondered how AI engineers fine-tune and align large language models? Under the hood, they run Supervised Fine-Tuning (SFT), optimize parameters using direct preference gradients (DPO), filter out low-quality pre-training corpuses (Pruning), and mitigate catastrophic drifts. To help you visualize how LLM alignment and parameter optimization work in a highly strategic way, I built a cyberpunk card battler inspired by Gwent: 🤖 Epoch Duel: Cyberpunk LLM Alignment Battle Play in Fullscreen Mode (if the embed sizing is tight) 🛠️ Tune Your Model Parameters Your mission as an alignment engineer is to play optimizer cards to outscore the adversarial baseline AI across 3 training Epochs: ⚙️ Logic & Coding: Run SFT code snippets, compile theorem provers, and deploy Python scripts to build your coding benchmark scores. 📖 Language & Speech: Train on multilingual datasets and summarization corpuses to maximize reading comprehension. 🛡️ Safety & Alignment: Implement red-team safeguards, configure RLHF preference pairs, and run DPO tuning to protect your model's outputs. ⚡ regularizers & Drifts: Deploy Regularization cards like Gradient Clipping (Scorch) and Model Pruning to destroy anomalies, or exploit Anomalous Drifts to collapse the AI's rows. 🧬 Playable ML Concepts Explained Here is how the card battle mechanics map to production machine learning pipelines: 1. ✂️ Model Pruning (Weight Compression) In-Game: Playing the Model Pruning card triggers a glitchy dissolution animation that purges the lowest-value card from the targeted board row, cleaning up noise. 💾 The Real-World Counterpart Model Pruning removes unimportant weights (often those closest to zero) from a trained neural network. It shrinks the memory footprint of the model, allowing it to run faster on edge devices. ⚠️ How it affects LLMs By stripping out low-impact weights, pruning compresses models by 30-50% with minimal loss in benchmark accuracy, making deployment significantly cheaper. 2. 🔀 DPO vs RL
I'm building TrainWiz , a Flutter app that turns real exercise into a pet-raising game: you do squats or push-ups, your phone counts the reps, and a little creature levels up and evolves. The core technical problem sounds trivial and absolutely is not: count reps from the camera, on-device, without uploading a single frame. Here's what broke along the way, and what finally worked. Why on-device Two reasons: privacy and latency. A fitness camera that streams your body to a server is a non-starter for most people, and rep feedback has to feel instant or the whole "game" loop dies. So everything runs locally with tflite_flutter + an on-device pose model — no footage ever leaves the phone. Naive attempt #1: joint-angle thresholds The obvious approach: track the knee angle, count a rep when it dips below X° and comes back up. // looks fine in a demo, dies in the real world final kneeAngle = angleBetween ( hip , knee , ankle ); if ( ! _down && kneeAngle < 100 ) _down = true ; if ( _down && kneeAngle > 160 ) { reps ++ ; _down = false ; } It demos beautifully. Then real users prop the phone on the floor, stand at an angle, and it falls apart. The trap: a phone camera gives you 2D pose. A "120° knee angle" flattens completely depending on where the camera sits — the same squat reads as 90° or 150° purely from perspective. Lifting to 3D via the model's z doesn't save you either; monocular z is noisy enough that the angle jitters across your threshold and double-counts. Naive attempt #2: a "body-line" gate Next idea: figure out which exercise you're doing so I can pick the right signal. Standing (squat) vs. horizontal (push-up) should be easy — just check if shoulder, hip and heel form a straight line, right? Wrong, again for the 2D reason. In a real push-up shot from the front-corner, shoulder–hip–heel are not collinear on the image plane — perspective bends them. I gated push-up counting on "body is a straight line" and it would just... stop counting mid-set. Nothing is more
The call-order change came back pass-with-risk. I read the recommendation, saw it had a name and a reason, and felt the task close. Then I looked at the row under it. How was this verified: not run. Nobody had run the queue. I had a label. I did not have proof. This is Part 6 of The Contract Think produced a brief. Plan produced a gate. Build executed inside it. Review scored every requirement against a verdict instead of an impression. Review reads the diff and the plan and decides whether one satisfies the other. It does not run the queue. It cannot. Its whole job is judgment about what the code should do. Test is where someone finally checks what the code actually does. I had been treating those two as the same step. They are not. Test asks one question, and a verdict is not the answer For every active requirement, Test asks how it was verified. Command run, manual QA, or a comparison against known-good output. One of those three, or a written reason none of them ran. Not a recommendation. Not a risk level. Evidence. I built the matrix against the plan's requirements and filled in each row. Most had a command behind them. The call-order requirement had nothing. The cell read not run, and it sat directly below a pass-with-risk that already carried a name and a reason. That name had almost been enough for me. A named risk feels handled. It is not. It is a risk with a label on it, waiting for someone to actually look. So I ran the queue Three notifications, all with a real reason to fire within the same tick. The scheduler picked them up and ordered them by priority instead of arrival. Two landed in the sequence the requirement wanted. The third jumped ahead of a lower-priority notification that was still mid-processing. The change worked almost every time. Under one timing condition, it did not. That is the gap a verdict cannot see. Review had marked the requirement partial because the wording left the mechanism open. Running it found a real failure inside the mech
A few days ago, I was talking to a junior developer who was literally sweating bullets. He had just pushed a feature for a staging website that barely gets 500 users a month. But looking at his senior developer’s reaction? You’d think the guy was managing the infrastructure for Amazon’s Prime Day Sale. “Scale check kiya? What if 10,000 users hit this exact API at 3 AM? Refactor this logic.” The code was perfectly fine for their current requirement. But the senior dev had to find a flaw to justify his hierarchy. This is where the tragedy of modern software engineering begins, and a brilliant, toxic survival hack takes over: The Placebo Bug. What is a Placebo Bug? (The Strategic Distraction) When experienced developers realize that their managers or seniors have a habit of “kami nikalna” (finding faults just for the sake of it), they stop giving them perfect code. Instead, they intentionally leave a very small, harmless, and obvious mistake in the front-end or the script. Maybe an unaligned button. Maybe a funny typo in an error message (like writing “Succesfully” instead of “Successfully”). Maybe a massive padding that makes the UI look slightly weird. When the senior reviews the code, their eyes immediately light up. “Arey! Look at this alignment. Everything else is fine, but fix this button first.” The junior says, “Sorry, my bad. Fixing it right away.” Two minutes later, a new commit is pushed. The senior feels proud that they added value, the junior’s core complex architecture passes without unnecessary refactoring, and everyone goes home happy. It’s not good engineering; it’s human management. This is actually a very old trick in the tech world, famously known as “The Corporate Duck” story. Years ago, a game designer noticed that his manager always forced changes on every project just to prove he was the boss. So, the designer tried a hack: he put a totally random, funny Duck on the main character’s head. The manager reviewed it and said, “Everything looks perfe
When searching for an affordable electric vehicle these days, there are always tradeoffs. How much range are you willing to sacrifice, how much leg room and storage space, how many features, in the pursuit of that magic sticker price that won't break the bank? The Fiat Topolino is basically the ultimate embodiment of those tradeoffs. […]
When it comes to achieving artificial general intelligence (AGI), large language models just don’t have what it takes. Models like ChatGPT and Claude are great at text, but they’re less skilled at understanding how things actually move through space and time — an essential skill for producing intelligence that generalizes. That gap, it turns out, might be filled by gaming data. That’s the bet behind General Intuition, a […]
US military drone losses in Iran war spur Pentagon call for cheap replacements.
A little robotic switch-flipper has become my sidekick in combating cockroaches. Before I got the SwitchBot Bot Rechargeable, I'd tiptoe through the dark every morning, hoping I wouldn't step on one of those terrible bugs scurrying around as I made my way to the light switch across the room. Now I'm ready for battle before […]
Android Bench is evolving, and developers can help guide that process.
For Paradigm, the technical frontier will stretch beyond its cryptocurrency investment roots. This fund is expected to expand its investment focus to include robotics and AI.
Founded in 2024, Prime Intellect’s goal is to give organizations capabilities to train their own agentic systems without relying on frontier AI labs.
Renders of multiple new Samsung devices have arrived a few weeks before the company's next Unpacked event.
I tried to build an arbitrage bot between Kalshi and Polymarket. Sports seemed to be the easiest because the matcher is relatively easy compared to the other markets (economy, bitcoin, weather, etc.) The matcher worked, we got about ~98% of the sports and e-sports market. But there's barely any sizable arbitrage between Kalshi and Polymarket, and what shows up closes in under 10 seconds. For the Argentina vs Egypt, the match with the disputed VAR call and Argentina's stoppage-time comeback from two goals down. Every price swing on that match, including the two around the VAR call, closed inside about 45 seconds. Total arbitrage opportunity net of fees across the whole match: $439, against $20.8 million moving through Polymarket's market alone and $13.8 million in Kalshi open interest. ( https://dino.markets/blog/argentina-egypt-var-price-gap ) That's not an arbitrage opportunity. That's what an efficient pair of order books looks like once you finally have the tooling to watch them at the same time. I logged this properly over a full day too: 870 cross-venue price gaps in one 24-hour window, median time open about 9 seconds, 96 percent closed inside 30. ( https://dino.markets/blog/how-long-a-mispricing-lasts ). So I shipped the Polymarket-Kalshi sports market matcher as an API instead of an arbitrage trading bot. It turned out the matcher itself was the useful part. Free REST access to the matched feed and the confirmed-arb view, 60 requests a minute, MCP server included so an agent can read it without you writing a client. Planning to open source the matching engine itself at some point. After that, either extend it to other market pairs between Kalshi and Polymarket, or look at arbitrage against traditional sportsbooks. Nothing locked in yet. Feel free to use it and tell me what you think about it. Thanks!
There are a lot of fast-growing AI startups, but some are growing even faster, they say.
The robotaxi stopped, called 911, and waited for the San Mateo Police to show up.