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IT'S NOT THAT HIS FABLE 5 IS MORE CAPABLE THAN YOURS. IT'S THAT HE LETS IT KEEP WORKING. MOST PEOPLE BUY AN AI BUILT TO OPERATE FOR DAYS THEN SHUT IT DOWN AFTER A FEW MINUTES. Nine out of ten users open a chat, assign one task, and close... the window. He gives Fable 5 a long-term objective and walks away. It breaks the job into smaller pieces, delegates work to sub-agents, reviews its own outputs with vision, and keeps moving without waiting for another prompt. While you're typing your next request, his system has already finished another task and improved its workflow along the way. The agent that creates the work never decides if it's good enough. A separate evaluator checks every result, and the process repeats until the required standard is met. At $10 per million input tokens and $50 per million output tokens, you pay only for the work completed not a monthly subscription. While he was away, the system generated $7,800 worth of results. Meanwhile, others spent the week opening chats and closing tabs. Most people see a more expensive AI model. He sees an autonomous worker that handles a week's worth of tasks every single day and keeps going long after everyone else has logged off. You finished reading this. The question is whether you'll put it to work or just scroll on.show more

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Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.

Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.

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ANTHROPIC'S HEAD OF ENGINEERING SAYS THEY JUST SHIPPED THE MOST CAPABLE MODELS THEY HAVE EVER BUILT Mythos 5 and Fable 5. Fable 5 can run for days on a single goal and get it right on the first pass, on work that used to need an entire team. Most people use AI to summarize things. The real leverage is the long autonomous task you keep putting off.

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Wayne Gretzky shares the advice he gives to every young athlete. "The commitment and the work ethic is worth it." "My dad was a big believer that the harder you work as a kid, the more fun you're going to have as an adult. And the more fun you have as a kid, the harder you're going to work as an adult." Read that again. It's one of the truest things ever said about success. Work now, play later. Or play now and work forever. "The commitment and the passion for what you're doing is so worth it at the end of the day. Just believe each and every day - it's really, really worth it." Commitment is a choice. It's choosing long-term growth over short-term comfort. "I wouldn't trade my life in for anything in the world." Outcomes and rewards require sacrifice. The key question is: what price am I willing to pay (🎥Be Better Off Show)

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CLAUDE FABLE 5 IS BACK, BUT IS IT THE SAME POWER OR WAS IT QUIETLY TRIMMED Fable 5 is available again. Anthropic's most powerful model is back globally for every paid account When it first landed three weeks ago, the difference was obvious right away. It didn't just help, it carried the task to the finish: entire web projects, presentations, reports, analysis. People dropped their work on other models and simply waited for Fable And the wait happened because three days after launch the US hit the model with export controls. There was no clean way to cut off foreign nationals, so Anthropic shut access down for everyone Now the restrictions are lifted, and here are the terms it came back on: > until July 7 it's included in a plan and covers up to 50% of the weekly limit > after that only through credits, topped up in settings > output runs at 50 dollars per million tokens Anthropic says it's the same model, just with stronger protection against cybersecurity use. Because the filter is so strict, there will be false positives now and then, and a request will get bumped down to a weaker model Whether it's the same power or quietly trimmed will only show after a couple of weeks of real work

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Mikadzyki🌙

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Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below

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Om Patel

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AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.

AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.

Mide

20,759 Aufrufe • vor 9 Tagen

ANTHROPIC'S PRODUCT CHIEF HAS USED CLAUDE FABLE 5 FOR MONTHS BEFORE ANYONE ELSE. HERE'S WHAT HE LEARNED ABOUT THE MOST POWERFUL MODEL YET Mike Krieger co-founded Instagram and now runs product at Anthropic. He's had Claude Fable 5 for two months before the public, and his takeaway is that it changes how you have to work, not just how much you get done. Here's what stood out, and what to actually do with it 1. It holds the whole project, so stop chopping tasks small. The old habit was breaking work into model-sized pieces and stitching them. Fable keeps the whole thing in context. What to do: stop pre-slicing your prompts into tiny steps. Hand it the full goal and the intent behind it, the way you'd brief a senior engineer, and let it sequence the work itself 2. Delegate big, async, and overnight. He sets it on a hard task at night and wakes to it finished, including the model getting itself unstuck when a service died, scaffolding a workaround, and documenting it. What to do: stop babysitting one prompt at a time. Kick off long jobs and walk away. Run several sessions at once instead of one you watch 3. The skill is planning now, not typing. His day moved to long architecture conversations up front, then execution in chunks. What to do: spend your first prompts planning, not building. Then ask it to output an HTML page or markdown doc of the plan so your team aligns before any code is written. That early alignment is the new leverage 4. Match the effort level to the task. Fable's range is wide, so a heavy reasoning pass on a tiny UI tweak is overkill (and pricey). What to do: dial effort down for small jobs, save the deep thinking for hard ones. And don't use your most expensive model for quick questions, keep a fast model for those 5. Verification is the real bottleneck now. The hard part isn't getting output, it's trusting it. What to do: make every change ship with proof. Have Claude attach a screenshot or video of what it built, so you can see the result instead of reading the diff. Then stand behind the decisions yourself before you merge 6. Cost is per-result, not per-turn. Fable is expensive per call but often one-shots what other models need ten turns to get right. What to do: judge cost by what it takes to finish the task to your satisfaction, not the price of a single message. Give it a real task and see how far it gets before you jump in His bigger point: software engineering isn't over, it's different. The craft moved from writing code to owning intent, taste, and what actually ships. The floor rose so anyone can build, and the ceiling rose so experts go further than before Bookmark this

ANTHROPIC'S PRODUCT CHIEF HAS USED CLAUDE FABLE 5 FOR MONTHS BEFORE ANYONE ELSE. HERE'S WHAT HE LEARNED ABOUT THE MOST POWERFUL MODEL YET Mike Krieger co-founded Instagram and now runs product at Anthropic. He's had Claude Fable 5 for two months before the public, and his takeaway is that it changes how you have to work, not just how much you get done. Here's what stood out, and what to actually do with it 1. It holds the whole project, so stop chopping tasks small. The old habit was breaking work into model-sized pieces and stitching them. Fable keeps the whole thing in context. What to do: stop pre-slicing your prompts into tiny steps. Hand it the full goal and the intent behind it, the way you'd brief a senior engineer, and let it sequence the work itself 2. Delegate big, async, and overnight. He sets it on a hard task at night and wakes to it finished, including the model getting itself unstuck when a service died, scaffolding a workaround, and documenting it. What to do: stop babysitting one prompt at a time. Kick off long jobs and walk away. Run several sessions at once instead of one you watch 3. The skill is planning now, not typing. His day moved to long architecture conversations up front, then execution in chunks. What to do: spend your first prompts planning, not building. Then ask it to output an HTML page or markdown doc of the plan so your team aligns before any code is written. That early alignment is the new leverage 4. Match the effort level to the task. Fable's range is wide, so a heavy reasoning pass on a tiny UI tweak is overkill (and pricey). What to do: dial effort down for small jobs, save the deep thinking for hard ones. And don't use your most expensive model for quick questions, keep a fast model for those 5. Verification is the real bottleneck now. The hard part isn't getting output, it's trusting it. What to do: make every change ship with proof. Have Claude attach a screenshot or video of what it built, so you can see the result instead of reading the diff. Then stand behind the decisions yourself before you merge 6. Cost is per-result, not per-turn. Fable is expensive per call but often one-shots what other models need ten turns to get right. What to do: judge cost by what it takes to finish the task to your satisfaction, not the price of a single message. Give it a real task and see how far it gets before you jump in His bigger point: software engineering isn't over, it's different. The craft moved from writing code to owning intent, taste, and what actually ships. The floor rose so anyone can build, and the ceiling rose so experts go further than before Bookmark this

Yarchi

30,958 Aufrufe • vor 3 Monaten

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.

cvxv666

24,298 Aufrufe • vor 2 Monaten

THIS GUY BUILT A 24/7 AI COMPANY WITH 7 AGENTS THAT CHECKS 48 TIMES A DAY, PICKS ITS OWN TASKS AND KEEPS WORKING WHILE HE SLEEPS 00:05 the map shows agents moving between the marketplace, research lab and command center, with each one handling a different job inside the same shared system the setup runs on 4 core layers: automatic triggers, shared memory, connected tools and an isolated verifier that checks every result before the next step starts each cycle reads the last 10 log entries, updates open tasks, saves new signals and passes the context forward, so the next agent never starts from zero one agent researches, two execute, another monitors outcomes and a read-only checker reviews the work independently, allowing several workflows to run in parallel without breaking the system bookmark this before everyone stops prompting one agent at a time and starts building AI teams that work, learn and improve around the clock

THIS GUY BUILT A 24/7 AI COMPANY WITH 7 AGENTS THAT CHECKS 48 TIMES A DAY, PICKS ITS OWN TASKS AND KEEPS WORKING WHILE HE SLEEPS 00:05 the map shows agents moving between the marketplace, research lab and command center, with each one handling a different job inside the same shared system the setup runs on 4 core layers: automatic triggers, shared memory, connected tools and an isolated verifier that checks every result before the next step starts each cycle reads the last 10 log entries, updates open tasks, saves new signals and passes the context forward, so the next agent never starts from zero one agent researches, two execute, another monitors outcomes and a read-only checker reviews the work independently, allowing several workflows to run in parallel without breaking the system bookmark this before everyone stops prompting one agent at a time and starts building AI teams that work, learn and improve around the clock

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19,071 Aufrufe • vor 2 Monaten

9 out of 10 multi-agent projects never leave demo mode Not because the model is bad. Because the structure is missing. Most people who try to build [ a team of AI agents ] end up with one agent talking to itself in five tabs. The agents don't share context → Don't divide work → Don't know what the others are doing. > stage 1: if your agent doesn't have a real loop, observe, act, iterate, you have a long prompt, not an agent > stage 2: subagents need isolated context, the orchestrator never reads their raw transcript, only the summary > stage 3: the orchestrator plans and delegates, the moment it executes, it drowns in details that belong inside subagents > stage 4: without a shared task list it's not a team, it's five agents duplicating each other's work in parallel > stage 5: a permissions file is what lets you sleep, the model cannot bypass it because the rule lives outside the model a team of AI agents is not more model, it is more structure the most ignored stage in every demo that died: - durability... when a 50-step task crashes at step 47 and starts from zero, that's not a model failure. that's a missing write-to-disk call

9 out of 10 multi-agent projects never leave demo mode Not because the model is bad. Because the structure is missing. Most people who try to build [ a team of AI agents ] end up with one agent talking to itself in five tabs. The agents don't share context → Don't divide work → Don't know what the others are doing. > stage 1: if your agent doesn't have a real loop, observe, act, iterate, you have a long prompt, not an agent > stage 2: subagents need isolated context, the orchestrator never reads their raw transcript, only the summary > stage 3: the orchestrator plans and delegates, the moment it executes, it drowns in details that belong inside subagents > stage 4: without a shared task list it's not a team, it's five agents duplicating each other's work in parallel > stage 5: a permissions file is what lets you sleep, the model cannot bypass it because the rule lives outside the model a team of AI agents is not more model, it is more structure the most ignored stage in every demo that died: - durability... when a 50-step task crashes at step 47 and starts from zero, that's not a model failure. that's a missing write-to-disk call

Shadow Nick

14,714 Aufrufe • vor 3 Monaten

Claude Fable 5 early tester: "If you're using Fable 5 for everything, that is almost 100% overkill." in 10 minutes, Nate breaks down the six prompting habits that actually get the most out of it here's what he covers: > always give the why, not just the what > negative prompting: tell it exactly what not to do > baking verification loops into every agent and skill file > the one prompt type that silently downgrades you to a weaker model most people are just throwing longer prompts at a more expensive model the ones getting real results changed how they prompt, not how much they spend an article on how to build a fully automated AI company with Claude Fable 5 👇

Claude Fable 5 early tester: "If you're using Fable 5 for everything, that is almost 100% overkill." in 10 minutes, Nate breaks down the six prompting habits that actually get the most out of it here's what he covers: > always give the why, not just the what > negative prompting: tell it exactly what not to do > baking verification loops into every agent and skill file > the one prompt type that silently downgrades you to a weaker model most people are just throwing longer prompts at a more expensive model the ones getting real results changed how they prompt, not how much they spend an article on how to build a fully automated AI company with Claude Fable 5 👇

rewind

11,868 Aufrufe • vor 2 Monaten

A developer in Hangzhou runs an AI that remembers everything about him for $0.40 a year. No vector database. One file that never grows past 4,000 tokens. He published the whole schema. His version starts from the opposite idea. Memory is not storage. It's a write policy. Six fields. Rewritten every time, never appended: > IDENTITY - who you are, what you build. 300 tokens. Changes monthly at most > STATE - what you're on right now. 400 tokens. Rewritten daily > DECISIONS - what's already settled, so nothing gets re-argued. 800 tokens > CORRECTIONS - every time you said "no, not like that." 600 tokens > PEOPLE - names, roles, who's waiting on what. 500 tokens > DEAD - tried and abandoned, so it never comes back as a suggestion. 400 tokens Three thousand tokens. Ceiling of four. When a section fills, the model rewrites it shorter. Nothing is ever added. Only replaced. Kimi K2.5 bills $0.10 per million cached input tokens. Four thousand tokens a turn is $0.0004. That's 2,500 turns for a dollar. The free tier hands you 1.5 million tokens a day. 375 turns before you pay anything at all. CORRECTIONS is the field nobody builds, and it's the one that does the work. A model that remembers being wrong stops repeating it. Everyone else is paying to search their own history. He pays to keep it short. The bill stopped growing when the file did. Your memory system isn't defined by what it stores. It's defined by what it agrees to delete. The article below is the full build - schema, rewrite prompts, the compaction rule that keeps it under the cap. Save it. You'll want it open in the other tab.

A developer in Hangzhou runs an AI that remembers everything about him for $0.40 a year. No vector database. One file that never grows past 4,000 tokens. He published the whole schema. His version starts from the opposite idea. Memory is not storage. It's a write policy. Six fields. Rewritten every time, never appended: > IDENTITY - who you are, what you build. 300 tokens. Changes monthly at most > STATE - what you're on right now. 400 tokens. Rewritten daily > DECISIONS - what's already settled, so nothing gets re-argued. 800 tokens > CORRECTIONS - every time you said "no, not like that." 600 tokens > PEOPLE - names, roles, who's waiting on what. 500 tokens > DEAD - tried and abandoned, so it never comes back as a suggestion. 400 tokens Three thousand tokens. Ceiling of four. When a section fills, the model rewrites it shorter. Nothing is ever added. Only replaced. Kimi K2.5 bills $0.10 per million cached input tokens. Four thousand tokens a turn is $0.0004. That's 2,500 turns for a dollar. The free tier hands you 1.5 million tokens a day. 375 turns before you pay anything at all. CORRECTIONS is the field nobody builds, and it's the one that does the work. A model that remembers being wrong stops repeating it. Everyone else is paying to search their own history. He pays to keep it short. The bill stopped growing when the file did. Your memory system isn't defined by what it stores. It's defined by what it agrees to delete. The article below is the full build - schema, rewrite prompts, the compaction rule that keeps it under the cap. Save it. You'll want it open in the other tab.

wast3

15,862 Aufrufe • vor 28 Tagen

$"put me on bro" for the next 6 months you're going to spend more time at your desk than with your friends and family you are going to miss events, turn down plans and work when you don't feel like it you are going to wake up stressed, go to sleep thinking about the goal, and question whether it's even worth it after all that, you are going to take every dollar you've saved and put it right back into business knowing there is a chance it doesn't work you are going to watch other people enjoy their money while you are risking yours and after all of that sacrifice, all of that work, all of that uncertainty, you are still only a fraction of the way of becoming successful$

"put me on bro" for the next 6 months you're going to spend more time at your desk than with your friends and family you are going to miss events, turn down plans and work when you don't feel like it you are going to wake up stressed, go to sleep thinking about the goal, and question whether it's even worth it after all that, you are going to take every dollar you've saved and put it right back into business knowing there is a chance it doesn't work you are going to watch other people enjoy their money while you are risking yours and after all of that sacrifice, all of that work, all of that uncertainty, you are still only a fraction of the way of becoming successful

alexei

48,580 Aufrufe • vor 2 Monaten

The annoying part of AI content is not making one clip. It is getting a rough idea through the entire production line without losing half the day between tabs. Script. Voiceover. Visuals. Music. Assembly. Approval. Publishing. Then the boring ops layer after it: organizing, managing and keeping the workflow connected to the tools where the business runs. That is the gap Scotty is going after. It works less like a single AI toy and more like a creative director plus operations copilot. You give it the brief, approve the plan, and it turns the idea into a finished creative asset while staying connected to apps like Shopify, Gmail and Google Sheets. That is the version of AI tools creators actually need. Not another tab for one task. A workflow that keeps moving after the first prompt.

The annoying part of AI content is not making one clip. It is getting a rough idea through the entire production line without losing half the day between tabs. Script. Voiceover. Visuals. Music. Assembly. Approval. Publishing. Then the boring ops layer after it: organizing, managing and keeping the workflow connected to the tools where the business runs. That is the gap Scotty is going after. It works less like a single AI toy and more like a creative director plus operations copilot. You give it the brief, approve the plan, and it turns the idea into a finished creative asset while staying connected to apps like Shopify, Gmail and Google Sheets. That is the version of AI tools creators actually need. Not another tab for one task. A workflow that keeps moving after the first prompt.

Kiyoro

10,417 Aufrufe • vor 2 Monaten

To be a successful founder, you have to believe that what you're working on is going to work — despite knowing it probably won't! That sounds like an oxymoron, but it's really not. Believing that what you're building is going to work is an essential component of coming to work with the energy, fortitude, and determination it's going to require to even have a shot. Knowing it probably won't is accepting the odds of that shot. It's simply the reality that most things in business don't work out. At least not in the long run. Most businesses fail. If not right away, then eventually. Yet the world economy is full of entrepreneurs who try anyway. Not because they don't know the odds, but because they've chosen to believe they're special. The best way to balance these opposing points — the conviction that you'll make it work, the knowledge that it probably won't — is to do all your work in a manner that'll make you proud either way. If it doesn't work, you still made something you wouldn't be ashamed to put your name on. And if it does work, you'll beam with pride from making it on the basis of something solid. The deep regret from trying and failing only truly hits when you look in the mirror and see Dostoevsky staring back at you with this punch to the gut: "Your worst sin is that you have destroyed and betrayed yourself for nothing." Oof. Believe it's going to work. Build it in a way that makes you proud to sign it. Base your worth on a human on something greater than a business outcome.

To be a successful founder, you have to believe that what you're working on is going to work — despite knowing it probably won't! That sounds like an oxymoron, but it's really not. Believing that what you're building is going to work is an essential component of coming to work with the energy, fortitude, and determination it's going to require to even have a shot. Knowing it probably won't is accepting the odds of that shot. It's simply the reality that most things in business don't work out. At least not in the long run. Most businesses fail. If not right away, then eventually. Yet the world economy is full of entrepreneurs who try anyway. Not because they don't know the odds, but because they've chosen to believe they're special. The best way to balance these opposing points — the conviction that you'll make it work, the knowledge that it probably won't — is to do all your work in a manner that'll make you proud either way. If it doesn't work, you still made something you wouldn't be ashamed to put your name on. And if it does work, you'll beam with pride from making it on the basis of something solid. The deep regret from trying and failing only truly hits when you look in the mirror and see Dostoevsky staring back at you with this punch to the gut: "Your worst sin is that you have destroyed and betrayed yourself for nothing." Oof. Believe it's going to work. Build it in a way that makes you proud to sign it. Base your worth on a human on something greater than a business outcome.

DHH

96,644 Aufrufe • vor 1 Jahr

BREAKING: Anthropic just dropped Fable 5.1—and CLAUDE IS SO BACK. We’ve spent the last week testing it at Every 📧 across coding, writing, and knowledge work. Our verdict: It's finally Fable for everyone. It’s the strongest coding model we’ve used, but now it's fast, token-efficient, and CRUCIALLY actually speaks like a normal person. Here’s our vibe check: - A monster at coding. Kieran Klaassen rebuilt a working version of Proof, our document editor, from one prompt. It added useful details he hadn’t requested, and it handles enormous coding jobs that run for days at a time. It built a computer use Mac app for me called Hands in one-shot that other models failed at. - A Claude our writers want to use again. It has clearer prose, fewer AI tells, and it takes an edit without arguing. It's a significant upgrade over Opus 5. And won Katie Parrott's heart back. - About half the tokens as Opus 5, and much faster. In our Slack-agent tests, it delivered comparable results to Opus 5 using about half as many tokens, in about 60% of the time. - Knowledge work you can delegate. It can produce great knowledge work—like slide decks—end to end without making slop. And flew threw hammer's tests with flying colors. - It now supports zero-data-retention agreements. Now businesses can actually use it! A big barrier to Fable adoption is gone. Net Result: It's obviously an Opus 5 killer. If that was your daily driver you should switch today. If you're using GPT-5.6 in ChatGPT for Work, it's spinning the wheels on for big delegated tasks. I still use ChatGPT for Work more day to day, but I use way more tokens in Fable 5.1. I send it off at the beginning of the day to do big programming projects, like end to end MVP builds, and check in every once in a while. State of Play: The big knock on Anthropic was they built a supergenius in a datacenter that was almost unusable. It was too slow, argued back, and talked in technical gibberish. They've managed to solve those problems and more with Fable 5.1!

BREAKING: Anthropic just dropped Fable 5.1—and CLAUDE IS SO BACK. We’ve spent the last week testing it at Every 📧 across coding, writing, and knowledge work. Our verdict: It's finally Fable for everyone. It’s the strongest coding model we’ve used, but now it's fast, token-efficient, and CRUCIALLY actually speaks like a normal person. Here’s our vibe check: - A monster at coding. Kieran Klaassen rebuilt a working version of Proof, our document editor, from one prompt. It added useful details he hadn’t requested, and it handles enormous coding jobs that run for days at a time. It built a computer use Mac app for me called Hands in one-shot that other models failed at. - A Claude our writers want to use again. It has clearer prose, fewer AI tells, and it takes an edit without arguing. It's a significant upgrade over Opus 5. And won Katie Parrott's heart back. - About half the tokens as Opus 5, and much faster. In our Slack-agent tests, it delivered comparable results to Opus 5 using about half as many tokens, in about 60% of the time. - Knowledge work you can delegate. It can produce great knowledge work—like slide decks—end to end without making slop. And flew threw hammer's tests with flying colors. - It now supports zero-data-retention agreements. Now businesses can actually use it! A big barrier to Fable adoption is gone. Net Result: It's obviously an Opus 5 killer. If that was your daily driver you should switch today. If you're using GPT-5.6 in ChatGPT for Work, it's spinning the wheels on for big delegated tasks. I still use ChatGPT for Work more day to day, but I use way more tokens in Fable 5.1. I send it off at the beginning of the day to do big programming projects, like end to end MVP builds, and check in every once in a while. State of Play: The big knock on Anthropic was they built a supergenius in a datacenter that was almost unusable. It was too slow, argued back, and talked in technical gibberish. They've managed to solve those problems and more with Fable 5.1!

Dan Shipper

204,342 Aufrufe • vor 18 Tagen

If you don't know what a RAG system is, you're already behind. Plenty of people are still typing questions into ChatGPT and pasting answers back out. That's the beginner level. You're renting a smart stranger who forgets you the second you close the tab and makes things up, because it has never seen your actual work. A RAG system is a completely different level of using AI. You build an AI plugged into your own world. Your files, company's docs, notes. It's not pulling from the internet, it only knows what you fed it. Ask it anything and it digs through your data, finds the answer, and shows you which document it came from. That's the gap. One person is asking a chatbot for generic answers. The other has built a private expert that knows their business cold and answers with receipts. And that gap is worth real money: > At work, the person who can wire a company's knowledge into an AI that actually answers correctly is rare and paid like it. > As a business, companies pay serious money for a system that reads their internal docs and answers staff and customers accurately. You build it once, you sell it. > For yourself, you get a second brain that actually remembers. Same skill behind all three, and it's still rare enough to be worth learning. Full breakdown in the article below.

If you don't know what a RAG system is, you're already behind. Plenty of people are still typing questions into ChatGPT and pasting answers back out. That's the beginner level. You're renting a smart stranger who forgets you the second you close the tab and makes things up, because it has never seen your actual work. A RAG system is a completely different level of using AI. You build an AI plugged into your own world. Your files, company's docs, notes. It's not pulling from the internet, it only knows what you fed it. Ask it anything and it digs through your data, finds the answer, and shows you which document it came from. That's the gap. One person is asking a chatbot for generic answers. The other has built a private expert that knows their business cold and answers with receipts. And that gap is worth real money: > At work, the person who can wire a company's knowledge into an AI that actually answers correctly is rare and paid like it. > As a business, companies pay serious money for a system that reads their internal docs and answers staff and customers accurately. You build it once, you sell it. > For yourself, you get a second brain that actually remembers. Same skill behind all three, and it's still rare enough to be worth learning. Full breakdown in the article below.

Yarchi

32,840 Aufrufe • vor 2 Monaten