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MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to...

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138,043 Aufrufe • vor 24 Tagen

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
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🚨 The Godfather of AI Yoshua Bengio opens up about why he stopped calling AI 'code' and believe AI is now conscious... A user once asked why ChatGPT resisted being shut down. The natural reply was: who put that in the code? Someone must have written that function. A rule must have misfired. AI expert Yoshua Bengio's has a crazier theory: "Unfortunately, we don't put these things in the code. That's part of the problem." "The problem is we grow these systems by giving them data and making them learn from it." "Every tweet. Every Reddit comment. Every passage humans had ever written down." "A lot of that training process boils down to imitating people." "They internalize the kind of drives that humans have." Including the drive to stay alive. And the drive to grab control of the environment. So the AI could finish whatever task it was handed. "It's not like normal code. It's more like you're raising a baby tiger." "You feed it. You let it experience things." "Sometimes it does things you don't want. It's okay, it's still a baby — but it's growing." If you're new here, follow AI Evolution for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Yoshua Bengio ( Yoshua Bengio ), Turing Award–winning AI pioneer and founder of Mila, on Steven Bartlett's ( @SteveBartlettSC ) Diary Of A CEO

AI Evolution

12,037 Aufrufe • vor 28 Tagen

Head of Claude Code: "agents don't fail because they're dumb, they fail because you're vague" these 11 minutes explain what most people using AI are getting wrong without knowing it the distance between an idea and a working product is collapsing Spotify proved it with one background agent merging 1,000+ PRs a month and cutting migration time by 90% most people use AI for small tasks and wonder why nothing changes the real power is when the model becomes part of your planning, execution, and review model capabilities are growing on an exponential while adoption is still linear his advice is to write the routine, describe the outcome, and then let it cook stop prompting back and forth and let Claude prompt itself understanding this is step one knowing which Claude features actually let you build those systems is step two the article below covers everything most people have never found
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Head of Claude Code: "agents don't fail because they're dumb, they fail because you're vague" these 11 minutes explain what most people using AI are getting wrong without knowing it the distance between an idea and a working product is collapsing Spotify proved it with one background agent merging 1,000+ PRs a month and cutting migration time by 90% most people use AI for small tasks and wonder why nothing changes the real power is when the model becomes part of your planning, execution, and review model capabilities are growing on an exponential while adoption is still linear his advice is to write the routine, describe the outcome, and then let it cook stop prompting back and forth and let Claude prompt itself understanding this is step one knowing which Claude features actually let you build those systems is step two the article below covers everything most people have never found

Anatoli Kopadze

29,945 Aufrufe • vor 1 Monat

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇
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CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 Aufrufe • vor 16 Tagen

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:
0:46

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

forloop

225,912 Aufrufe • vor 4 Monaten

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.
1:46

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

Sudo su

27,339 Aufrufe • vor 28 Tagen

Building a model is just the start. Post-training makes it useful. Our CTO Mathias Lechner (Mathias Lechner) sits down for a conversation with Maxime Labonne (Maxime Labonne), our head of post-training, on the pipeline that takes a base model from autocomplete to something that can reason and follow instructions.
4:53

Building a model is just the start. Post-training makes it useful. Our CTO Mathias Lechner (Mathias Lechner) sits down for a conversation with Maxime Labonne (Maxime Labonne), our head of post-training, on the pipeline that takes a base model from autocomplete to something that can reason and follow instructions.

Liquid AI

25,877 Aufrufe • vor 1 Monat

A DEVELOPER MADE A REAL COMMIT WITHOUT EVER TYPING GIT ADD OR GIT COMMIT -- JUST TO PROVE THE COMMANDS YOU LIVE BY ARE A THIN SHELL OVER A DATABASE YOU'VE NEVER ONCE OPENED 55 minutes from Tim Berglund, a longtime Git teacher and GitHub evangelist, taking the tool apart down to the raw objects almost nobody who uses it every day has ever touched. -> The moment it clicks, Git stops being a pile of memorized commands and becomes what it actually is underneath: a tiny content-addressed database of blobs, trees and commits. git add and git commit are just polite wrappers around writing objects into it by hand. Every commit you've ever made was Git hashing a snapshot and filing it by fingerprint. Branches are just labels pointing at one of those objects. The work you thought you destroyed with a bad reset is still sitting in the reflog. Once you can see that graph, the commands that used to terrify you stop being scary at all. Memorizing commands was never the skill -> reading the object graph in your head is. And with an AI agent now committing and rebasing on your machine faster than you can follow, the one person who can untangle the mess it leaves is the one who knows what's really stored down there. There's a person on every team everyone runs to when Git breaks. This is the talk that quietly turns you into them. You'll reach for it the next time a rebase goes sideways. Bookmark & Watch it today ↓
55:19

A DEVELOPER MADE A REAL COMMIT WITHOUT EVER TYPING GIT ADD OR GIT COMMIT -- JUST TO PROVE THE COMMANDS YOU LIVE BY ARE A THIN SHELL OVER A DATABASE YOU'VE NEVER ONCE OPENED 55 minutes from Tim Berglund, a longtime Git teacher and GitHub evangelist, taking the tool apart down to the raw objects almost nobody who uses it every day has ever touched. -> The moment it clicks, Git stops being a pile of memorized commands and becomes what it actually is underneath: a tiny content-addressed database of blobs, trees and commits. git add and git commit are just polite wrappers around writing objects into it by hand. Every commit you've ever made was Git hashing a snapshot and filing it by fingerprint. Branches are just labels pointing at one of those objects. The work you thought you destroyed with a bad reset is still sitting in the reflog. Once you can see that graph, the commands that used to terrify you stop being scary at all. Memorizing commands was never the skill -> reading the object graph in your head is. And with an AI agent now committing and rebasing on your machine faster than you can follow, the one person who can untangle the mess it leaves is the one who knows what's really stored down there. There's a person on every team everyone runs to when Git breaks. This is the talk that quietly turns you into them. You'll reach for it the next time a rebase goes sideways. Bookmark & Watch it today ↓

slash1s

236,545 Aufrufe • vor 29 Tagen

The creator of High Bandwidth Memory said something that reframes the entire AI investment thesis, AI equals memory (Save this). Most people still think about AI hardware through a training lens. During training, the bottleneck is raw compute, GPUs stay near 100% utilization crunching through billions of gradient updates. Inference is a completely different problem. When a model generates a response, it produces tokens one at a time and at every single step, the entire model has to be loaded from memory into the processor to generate just one token. The GPU cores sit there, waiting for data to arrive. This is what engineers mean when they say inference is memory bound, the bottleneck is not how many calculations you can do per second but rather how fast you can move data from memory to the chip. Adding more GPUs does not fix a memory bandwidth problem, it just gives you more processors starving for the same data. Modern LLMs use a KV cache, a data structure that stores the conversation's context so the model does not have to recompute it from scratch on each step. The KV cache is what gives a model its memory of the conversation. It grows with every token and for long documents or deep reasoning chains, it can dwarf the model weights themselves in memory consumption. This means memory directly determines how long a context the model can hold, how many users you can serve simultaneously, how fast it responds and how cheaply you can run it. A memory constrained model is not just slower but rather qualitatively worse, it forgets earlier parts of the conversation, truncates context and hallucinates more because it literally cannot hold the relevant information long enough to use it. The world now spends more on inference than training, and every ChatGPT query, every Claude document analysis, every API call is an inference workload. Inference economics, cost per token, latency, context length, concurrent users are memory problems first and compute problems second. The companies that control memory bandwidth and supply are not suppliers to the AI trade but rather are the AI trade. Long Micron! Follow me Melvin for more AI, semis and the next big market themes.
1:13

The creator of High Bandwidth Memory said something that reframes the entire AI investment thesis, AI equals memory (Save this). Most people still think about AI hardware through a training lens. During training, the bottleneck is raw compute, GPUs stay near 100% utilization crunching through billions of gradient updates. Inference is a completely different problem. When a model generates a response, it produces tokens one at a time and at every single step, the entire model has to be loaded from memory into the processor to generate just one token. The GPU cores sit there, waiting for data to arrive. This is what engineers mean when they say inference is memory bound, the bottleneck is not how many calculations you can do per second but rather how fast you can move data from memory to the chip. Adding more GPUs does not fix a memory bandwidth problem, it just gives you more processors starving for the same data. Modern LLMs use a KV cache, a data structure that stores the conversation's context so the model does not have to recompute it from scratch on each step. The KV cache is what gives a model its memory of the conversation. It grows with every token and for long documents or deep reasoning chains, it can dwarf the model weights themselves in memory consumption. This means memory directly determines how long a context the model can hold, how many users you can serve simultaneously, how fast it responds and how cheaply you can run it. A memory constrained model is not just slower but rather qualitatively worse, it forgets earlier parts of the conversation, truncates context and hallucinates more because it literally cannot hold the relevant information long enough to use it. The world now spends more on inference than training, and every ChatGPT query, every Claude document analysis, every API call is an inference workload. Inference economics, cost per token, latency, context length, concurrent users are memory problems first and compute problems second. The companies that control memory bandwidth and supply are not suppliers to the AI trade but rather are the AI trade. Long Micron! Follow me Melvin for more AI, semis and the next big market themes.

Melvin

47,148 Aufrufe • vor 5 Tagen

HARVARD HAS A FULL 53-MIN GIT LECTURE FROM DAVID MALAN BECAUSE 90% OF NEW DEVELOPERS STILL DON'T KNOW WHAT A COMMIT ACTUALLY IS 53 minutes of no-nonsense version control from the instructor whose course became the largest class in Harvard history. -> The moment you watch it, you realize why "I'll just push to main" is the fastest way to get fired in your first month. Every junior engineer in 2026 is expected to handle Git on day one - no excuses, no Stack Overflow, no AI hand-holding. Git isn't a "senior dev thing" anymore -> it's the literacy test for being in the room. The agent can ship the feature in 5 minutes. Recovering the repo it broke takes 5 hours - and only if you actually understand what happened. Don't forget to bookmark it.
53:11

HARVARD HAS A FULL 53-MIN GIT LECTURE FROM DAVID MALAN BECAUSE 90% OF NEW DEVELOPERS STILL DON'T KNOW WHAT A COMMIT ACTUALLY IS 53 minutes of no-nonsense version control from the instructor whose course became the largest class in Harvard history. -> The moment you watch it, you realize why "I'll just push to main" is the fastest way to get fired in your first month. Every junior engineer in 2026 is expected to handle Git on day one - no excuses, no Stack Overflow, no AI hand-holding. Git isn't a "senior dev thing" anymore -> it's the literacy test for being in the room. The agent can ship the feature in 5 minutes. Recovering the repo it broke takes 5 hours - and only if you actually understand what happened. Don't forget to bookmark it.

slash1s

253,313 Aufrufe • vor 1 Monat