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Sacha Feinberg-Mngomezulu is a cheat code 🤩 First half hat-trick completed inside 26 minutes 🔥 Vodacom #URC | #STOvCON

Vodacom United Rugby Championship (URC)

17,033 subscribers

19,200 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

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Remember that time you saw Sacha score a hat-trick in the first half? Everyone here will. #STOvCON #iamastormer Vodacom #URC

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What a way to bring up your hat-trick 🎩🤩 Sacha Feinberg-Mngomezulu today 🤯 #RSAvARG #TheRugbyChampionship

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Sacha Feinberg-Mngomezulu x 2029 🤩 The Springbok superstar is staying with the DHL Stormers 😍 #BKTURC #URC

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Allan Saint-Maximin got a hat-trick of assists inside 30 minutes 🔥

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16 year-old midfielder Khai Vally has scored a hat-trick for Fulham on the opening day of the U18 Premier League season.👀🎩 Vally scored two in the first half and completed his hat-trick late in the second half, helping Fulham beat Ipswich Town U18s 5-2.✅ Across the 13 U18PL fixtures today, Khai was the only player to bag a hat-trick.✨ What a performance.🤩👏 🎥/ Fulham Football Club

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A HAT TRICK IN HISTORY. 🎩🔥 Sydni Black has three goals in the first half of the first-ever WLL Championship, fueled by Gatorade. ⚓️

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The Bugged Dev

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We're getting to the point where AI isn't just helping us write code; it can help launch the business behind that code too. One of the more interesting examples I've come across is **Lovie** Instead of jumping between legal websites, paperwork, and incorporation services Lovie lets founders form a US LLC or Corporation directly from AI coding tools like **Claude Code, Cursor, and Windsurf** through its MCP-first workflow. If you're already building your product inside your AI coding assistant, the setup process can become part of that same workflow instead of another tab to manage. For founders, indie hackers, and developers shipping fast, that's a pretty compelling idea. Flat **$29/month**, company formation in minutes, and it fits naturally into the AI tools many developers are already using. Feels like another step toward AI becoming a true operating system for builders, not just a coding assistant.

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Parul Gautam

41,751 次观看 • 2 个月前

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ANI

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This Claude Code Skills Pack is a cheat code for ad creative teams 🤯 10 plug-and-play skills → competitor audits, creative briefs, 20 hook variations, ad copy, static ads, landing pages, & weekly performance reports. All inside Claude Code. Perfect for DTC brands and agencies who are still prompting Claude Code from scratch every time. If you're re-explaining your brand voice in every session, getting inconsistent output depending on who's prompting, and spending 30 minutes on tasks that should take 30 seconds... These skills eliminate the entire loop: → Competitor Ad Research Agent Drop a brand name, get back a full creative audit — hooks, messaging angles, ad formats, CTAs, and "steal this" angles. No more scrolling the Ad Library for an hour. → Creative Brief Generator One prompt, complete brief in your exact template. Hooks, concepts, visual direction, brand voice — all loaded from your own files. → Hook & Script Writer 15+ hooks categorized by type (curiosity, problem-agitation, result-first, social proof). Full 30-60s scripts with the hook → problem → mechanism → proof → CTA structure baked in. → Ad Copy Variation Engine Feed it one winning ad, get back 20 variations — each targeting a different persona and pain point. Same structure, different angles. Creative fatigue solved. → Weekly Report Writer Drop in your Meta ads CSV. Get back the narrative summary, anomaly flags, creative fatigue alerts, and recommended next steps. The report nobody wants to write, written in 60 seconds. → Creative Fatigue Detector Flags ads before they die. CTR trending down, frequency climbing, conversion rate dropping — caught in hours, not after three days of wasted spend. No prompting from scratch every time. No inconsistent output across your team. No re-explaining context in every session. I packaged all 10 as a free Skills Pack. Copy-paste the files into your Claude Code commands folder and they just work. Want the full Skills Pack? > Like this post > Comment "SKILLS" And I'll send it over (must be following so I can DM)

Mike Futia

56,066 次观看 • 6 个月前

On the day that the Cincinnati Bengals announce their blockbuster deal with Ja’Marr Chase, I got a story … In 2021, Chase hit a rough patch in camp with drops. It was a big story at the time, the kind that blow up in the summer. Anyway, I came thru for an early August practice, and hung out with coaches on the field for about a half-hour after the session ended. I turned around, and there was Ja’MarrChase, out there on the Juggs, having kept one teammate behind to help him. Five minutes later, he was by himself, with an equipment man shooting the balls at him. At the end of my camp trip, I showed my football-obsessed first-grader (now a fourth-grader) how this player picked fifth overall was responding in the face of adversity early in his career. Almost all the rest of the players and coaches were inside. It wasn’t a show. It was a guy consumed with being the best. Which is why I think the Bengals will sleep well writing him those checks.

On the day that the Cincinnati Bengals announce their blockbuster deal with Ja’Marr Chase, I got a story … In 2021, Chase hit a rough patch in camp with drops. It was a big story at the time, the kind that blow up in the summer. Anyway, I came thru for an early August practice, and hung out with coaches on the field for about a half-hour after the session ended. I turned around, and there was Ja’MarrChase, out there on the Juggs, having kept one teammate behind to help him. Five minutes later, he was by himself, with an equipment man shooting the balls at him. At the end of my camp trip, I showed my football-obsessed first-grader (now a fourth-grader) how this player picked fifth overall was responding in the face of adversity early in his career. Almost all the rest of the players and coaches were inside. It wasn’t a show. It was a guy consumed with being the best. Which is why I think the Bengals will sleep well writing him those checks.

Albert Breer

170,960 次观看 • 1 年前

A 17 year old high schooler told his mom he needed a Steam Deck for school. She said no, it's a gaming console. He said it runs Linux. She didn't know what that means. Bought it for his birthday. $280. He never installed a single game on it. Opened the terminal, installed Claude Code and typed his first command while holding the device like a PlayStation controller. Thumbsticks on both sides. Code editor in the middle. The most ridiculous dev setup anyone has ever seen. At second 0:09 you can read what he typed into the terminal: claude your code looks like absolute shit Claude didn't argue. Just started rewriting the shader, adding bloom effects, fixing chromatic aberration and improving the particle system. On a gaming console held in two hands on a couch. His friends play Fortnite on their Steam Decks. He builds software on his while lying in bed. He set up Claude Code with custom skills, hooks that auto run tests every time a file is saved and memory that remembers every project across sessions. The stuff most developers pay $200 a month for and use at maybe 20% capacity. He runs it on a $280 handheld and squeezes out every feature. Within three weeks he had built and sold four small apps to local businesses. A booking page for a barber shop, an inventory tracker for a vape store, a menu site for a taco truck and a scheduling tool for a dog groomer. All built on a Steam Deck in his bedroom. All coded by Claude while he gave instructions with his thumbs. Made over $13,000 in his first month. His mom still thinks he plays games on it. His teacher caught him using it during study hall. Looked at the screen expecting a game. Saw green code scrolling and Claude asking: Do you want to make this edit to main.js ? Teacher had no idea what she was looking at. Told him to put it away. He closed the lid. Claude kept running inside. A $280 gaming console that his mom bought thinking it was a toy is now a development workstation that earns more per month than her car payment. Setup time: 20 minutes once. Time he saves every day: 3 to 5 hours. Money made in month one: $13,000. Games installed: zero. His grandpa asked him to install FIFA last weekend. He said the console is busy. Grandpa asked doing what. He said working. Grandpa didn't ask again.

A 17 year old high schooler told his mom he needed a Steam Deck for school. She said no, it's a gaming console. He said it runs Linux. She didn't know what that means. Bought it for his birthday. $280. He never installed a single game on it. Opened the terminal, installed Claude Code and typed his first command while holding the device like a PlayStation controller. Thumbsticks on both sides. Code editor in the middle. The most ridiculous dev setup anyone has ever seen. At second 0:09 you can read what he typed into the terminal: claude your code looks like absolute shit Claude didn't argue. Just started rewriting the shader, adding bloom effects, fixing chromatic aberration and improving the particle system. On a gaming console held in two hands on a couch. His friends play Fortnite on their Steam Decks. He builds software on his while lying in bed. He set up Claude Code with custom skills, hooks that auto run tests every time a file is saved and memory that remembers every project across sessions. The stuff most developers pay $200 a month for and use at maybe 20% capacity. He runs it on a $280 handheld and squeezes out every feature. Within three weeks he had built and sold four small apps to local businesses. A booking page for a barber shop, an inventory tracker for a vape store, a menu site for a taco truck and a scheduling tool for a dog groomer. All built on a Steam Deck in his bedroom. All coded by Claude while he gave instructions with his thumbs. Made over $13,000 in his first month. His mom still thinks he plays games on it. His teacher caught him using it during study hall. Looked at the screen expecting a game. Saw green code scrolling and Claude asking: Do you want to make this edit to main.js ? Teacher had no idea what she was looking at. Told him to put it away. He closed the lid. Claude kept running inside. A $280 gaming console that his mom bought thinking it was a toy is now a development workstation that earns more per month than her car payment. Setup time: 20 minutes once. Time he saves every day: 3 to 5 hours. Money made in month one: $13,000. Games installed: zero. His grandpa asked him to install FIFA last weekend. He said the console is busy. Grandpa asked doing what. He said working. Grandpa didn't ask again.

Marlow

3,238,752 次观看 • 4 个月前

Polymarket introduced new fees to kill bots on 15-minute markets. Result? They just eliminated competition for the two apex predators. These wallets did not just survive. They mutated. While the platform tried to "fix" the system, these two made $918,357 in pure profit in one month. On the fees that were supposed to bankrupt them. Someone on Twitter was complaining his stops get blown at the exact same second every time. Said he was up against HFT bots with direct exchange connections. In the comments someone dropped two profiles. Said here are your "manipulators". Just code. $522,439 profit in one month. $65 million volume. First place on crypto leaderboard. This is not a person. This is PurpleThunderBicycleMountain. His profile: The second profile looked like it could not top the first. 0x8dxd. Second place. $395,918 in the same month. But here is what breaks the brain completely. In December 2025 this wallet had $313. Three hundred thirteen dollars. Now his all-time profit exceeds $658,000. The numbers do not lie. $313 turned into a fortune in six weeks. See for yourself: The mechanics are uncomfortably simple. Both trade the same thing. 15-minute crypto markets. Bitcoin up or down. ETH above or below. Every 15 minutes a new contract. Here is the trick. You look at Polymarket and see 50/50 odds. For you this is a fair coin flip. But these two do not look at Polymarket. They look at Binance. When BTC makes a move on the exchange Polymarket does not know yet. Price already moved. Direction already determined. But odds on the platform still show yesterday. The window lasts 30 seconds. Sometimes 90. It is like playing poker against someone with a mirror behind your back. He already sees your cards. You still think you are bluffing. During this time the bot enters at old prices. Buys YES at 25 cents when real probability is already 80%. Or NO at 1.5 cents when the market already crashed. Then the window closes. Odds catch up to reality. Bot gets $1 for every 25 cents. This is not forecasting. This is harvesting from people staring at a stale screen. One trade stands out. ETH dumped on spot. PurpleThunderBicycleMountain loaded NO at 1.5 cents. Kept buying as price rose to 15 cents. Window closed down. Every share paid $1. 785% in fifteen minutes. On one trade. The craziest part is 0x8dxd stats. Win rate 98%. Out of 6,615 trades. Almost perfect. A human cannot trade like this. A human has shaky hands. A human doubts. A human sleeps. The bot does not sleep. The bot does not doubt. The bot just waits for Binance and Polymarket to diverge for 30 seconds. And takes the difference. Their profit curves both grow without a single pullback. No crashes. No nervous breakdowns. Methodical. Mechanical. Relentless. Polymarket introduced fees to stop this. Result? Small bots died. These two took their market share. A 1.5% fee means nothing when your average return is 300%. Here is what kills. Anyone can spend months learning Python. Rent a server near the exchange. Write an algorithm. Test. Debug. Lose money on mistakes. Or just open their profiles and see what they are buying. The blockchain stores everything. Every position. Every timing. Full history in the open. They spend thousands on infrastructure. Write code for months. Optimize milliseconds. Anyone can just watch where they enter. $918,357 in one month between them. First and second place on the leaderboard. This is not theory. This is a working machine. Right now somewhere BTC is making a move. Polymarket still shows old odds. These two are already entering a position. Only one question remains: Will you be the one selling to them at stale prices? Or the one entering alongside them? In 15 minutes a new contract opens. 14 minutes left.

Blaze

61,835 次观看 • 7 个月前

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,478 次观看 • 6 个月前

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.

West Lord

24,679 次观看 • 2 个月前

Cat Playing With Its Mouse: The Meaning Behind China’s Radar Lock on Japan’s F-15J on December 6th There is real tension in the air between Japan and China. Washington is slowly retreating from the Western Pacific, abandoning the region to Japan and telling it to hold the line alone. The world has shifted. There was a time when China was weak and poor, when every bully felt entitled to trample it. Those days are over. China rose for one purpose: to never again be abused and bullied. And now, strong and self-assured, China can finally settle accounts with the old tormentors — and Japan is at the top of that list. But the age of war has changed. The modern battlefield is not measured by bayonets or trenches, but by fire-control radars, AI-driven targeting systems, and the kind of technological superiority that lets you toy with your opponent without firing a shot. And this is exactly what unfolded on December 6. The Fire-Control Radar Incident: A Gun to the Forehead Koizumi Shinzorō, Japan’s Prime Minister, wanted confrontation, and China responded with precision. On December 6, Japan’s Defense Minister publicly admitted that J-15s launched from the Liaoning carrier had twice locked Japan’s F-15Js with fire-control radar over the high seas southeast of Okinawa. He called it “dangerous” and expressed “deep regret.” He is right to feel danger. This was no accident, no miscalculation. This was the PLA presenting a complete offensive posture, signaling that the region is one fingertip away from war — and China is fully prepared to exercise their rights under UN Charter Article 107. Most people have no idea how deadly serious “fire-control illumination” is. Japan’s F-15J pilot certainly knew: his cockpit must have exploded into warning alarms; Imagine that shrill beeping screaming through the cockpit for half an hour - enough to drive any normal person mad; his breathing must have turned shallow; his hands probably shook as he tried to maneuver away from the lock. However, even under that level of crushing psychological pressure, the Japanese pilot chose to stay inside the zone rather than fleeing. This is kamicaze level provocation. Because the moment fire-control goes live, the radar narrows into a focused beam, feeding exact parameters to the missile under the jet’s wing. In peacetime exercises, a sustained lock counts as a confirmed kill. In real combat, nobody activates it unless they are ready to shoot. And China kept that beam on the F-15J for over half an hour. First lock: 16:32–16:35, three full minutes. Second lock: 18:37–19:08, more than thirty minutes. There is no suspense about how this confrontation would end in a real war. It would be a guaranteed kill. The Cat and the Mouse To be precise, the J-15 wasn’t merely locking and unlocking. It was playing — the way a cat toys with a mouse trapped under its paw. Japan’s F-15J was that mouse.

Cat Playing With Its Mouse: The Meaning Behind China’s Radar Lock on Japan’s F-15J on December 6th There is real tension in the air between Japan and China. Washington is slowly retreating from the Western Pacific, abandoning the region to Japan and telling it to hold the line alone. The world has shifted. There was a time when China was weak and poor, when every bully felt entitled to trample it. Those days are over. China rose for one purpose: to never again be abused and bullied. And now, strong and self-assured, China can finally settle accounts with the old tormentors — and Japan is at the top of that list. But the age of war has changed. The modern battlefield is not measured by bayonets or trenches, but by fire-control radars, AI-driven targeting systems, and the kind of technological superiority that lets you toy with your opponent without firing a shot. And this is exactly what unfolded on December 6. The Fire-Control Radar Incident: A Gun to the Forehead Koizumi Shinzorō, Japan’s Prime Minister, wanted confrontation, and China responded with precision. On December 6, Japan’s Defense Minister publicly admitted that J-15s launched from the Liaoning carrier had twice locked Japan’s F-15Js with fire-control radar over the high seas southeast of Okinawa. He called it “dangerous” and expressed “deep regret.” He is right to feel danger. This was no accident, no miscalculation. This was the PLA presenting a complete offensive posture, signaling that the region is one fingertip away from war — and China is fully prepared to exercise their rights under UN Charter Article 107. Most people have no idea how deadly serious “fire-control illumination” is. Japan’s F-15J pilot certainly knew: his cockpit must have exploded into warning alarms; Imagine that shrill beeping screaming through the cockpit for half an hour - enough to drive any normal person mad; his breathing must have turned shallow; his hands probably shook as he tried to maneuver away from the lock. However, even under that level of crushing psychological pressure, the Japanese pilot chose to stay inside the zone rather than fleeing. This is kamicaze level provocation. Because the moment fire-control goes live, the radar narrows into a focused beam, feeding exact parameters to the missile under the jet’s wing. In peacetime exercises, a sustained lock counts as a confirmed kill. In real combat, nobody activates it unless they are ready to shoot. And China kept that beam on the F-15J for over half an hour. First lock: 16:32–16:35, three full minutes. Second lock: 18:37–19:08, more than thirty minutes. There is no suspense about how this confrontation would end in a real war. It would be a guaranteed kill. The Cat and the Mouse To be precise, the J-15 wasn’t merely locking and unlocking. It was playing — the way a cat toys with a mouse trapped under its paw. Japan’s F-15J was that mouse.

America-China Watcher

21,361 次观看 • 9 个月前

A good technical LLM interview question: Your LLM chatbot takes 12s before it generates the first token, and the users are complaining. So you move the model onto a GPU with 3x the computing power. The time to first token barely improves. Why did this happen? (answer below) Latency in an LLM app is a placement problem disguised as a model problem. If you profile the 12 seconds, the model's prefill itself may only account for around 1.5 seconds of it. So halving the prefill step saves just 750ms out of 12000, which is under 7%. The rest is spread across stages that never touch the GPU. The request first travels to whatever region the app runs in, and a cross-continent round trip could cost over a second before any code executes. Then the request handler starts. On a container-based serverless platform under load, this adds several seconds of cold start, paid before auth, rate limiting, or prompt assembly even begins. Retrieval adds its own hop, and the response streams back across the same distance. Optimizing a stage that was already fast cannot alter the latency that's majorly affected by other stages. Those other stages are slow for a structural reason. An LLM app runs two workloads that want opposite machines. - The request path is short, spiky, and needs to sit close to users - Inference is long-running, GPU-bound, and billed hourly, whether requests arrive or not. So the actual decision is not which model to run, but where each of these two workloads runs. There are three options, each with its own tradeoffs: > A dedicated GPU box removes inference cold starts, but it bills around the clock and lives in one location, so distant users wait out the round trip on every request > Container-based serverless scales to zero, but the request path pays a cold start, and most of these platforms have no GPU behind them. > Edge runtimes start in under a millisecond, because a WebAssembly module carries no OS or container image to boot. They handle the request path well and cannot hold a model. So the answer is not to pick one, but to split the app across two of them. The request path runs close to users, and inference runs on a dedicated GPU it calls into. That also explains the failed upgrade. More compute made a stage that was already fast faster, and left the 10.5 seconds around it untouched. To actually learn how it's done in practice, Akamai's GitHub has a reference implementation for each half. - vllm-on-lke serves Qwen2.5-7B-Instruct behind an OpenAI-compatible endpoint on one RTX 4000 Ada GPU in Linode Kubernetes Engine, with Terraform creating the cluster, both firewalls, and the GPU operator in one apply. - akamai-functions-llm-chatbot covers the front, where a WebAssembly API checks a KV cache and only calls the GPU-backed instance on a miss. Both are available on Akamai’s new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post treats generation as a single 1.5s block, but that block has its own structure, and knowing it well tells you whether a model is slow to start or slow to stream. I wrote a first-principles walkthrough of it, covering the prefill and decode split, KV caching, and where the time actually goes inside each one. Read it below. Thanks to Akamai Cloud for partnering today!

Avi Chawla

21,786 次观看 • 1 个月前