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THIS IS HOW FABLE 5 MOVES WHEN YOUR VAULT IS BUILT RIGHT direct path. zero wandering. hits the target on the first try. without structure it's the opposite. 7 files opened. 2 minutes wasted. brief from 3 months ago still missing. one index file per major folder. gives the... show more

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SOMEONE MAPPED CHAOS INTO A NAVIGABLE SPACE AND YOUR HERMES AGENT NEEDS THE SAME THING thousands of chaotic data points, each given a position, navigation becomes instant because the structure does the work your vault is the same chaos, hermes lands in it and opens files at random because nothing tells it where to start one index file per major folder with a clear starting point changes everything 2 minutes per task drops to 10 seconds, same agent, same model full breakdown in the article below ↓

SOMEONE MAPPED CHAOS INTO A NAVIGABLE SPACE AND YOUR HERMES AGENT NEEDS THE SAME THING thousands of chaotic data points, each given a position, navigation becomes instant because the structure does the work your vault is the same chaos, hermes lands in it and opens files at random because nothing tells it where to start one index file per major folder with a clear starting point changes everything 2 minutes per task drops to 10 seconds, same agent, same model full breakdown in the article below ↓

leopardracer

23,690 görüntüleme • 3 ay önce

I MADE MY AI AGENT 10X FASTER WITHOUT CHANGING THE MODEL not a smarter model, not a bigger context window, not another clever prompt the same kind of AI that designs vaccines for viruses we have not even met yet was spending two minutes opening the wrong files just to hand me a brief from three months ago the problem was never capability, it was the scaffolding that piled up around my agent by accident, folder by folder an agent does not think in your categories, it searches from scratch every single time, and your tidy human folders are a maze to it the fix was almost stupidly small, one index file at the root of each big folder and a few numbers in front of the folder names slowest task dropped from 2 minutes to 26 seconds, fastest ones hit 10, zero model changes capability is cheap when the scaffolding around it is broken the article breaks down the whole system in 15 minutes ↓

I MADE MY AI AGENT 10X FASTER WITHOUT CHANGING THE MODEL not a smarter model, not a bigger context window, not another clever prompt the same kind of AI that designs vaccines for viruses we have not even met yet was spending two minutes opening the wrong files just to hand me a brief from three months ago the problem was never capability, it was the scaffolding that piled up around my agent by accident, folder by folder an agent does not think in your categories, it searches from scratch every single time, and your tidy human folders are a maze to it the fix was almost stupidly small, one index file at the root of each big folder and a few numbers in front of the folder names slowest task dropped from 2 minutes to 26 seconds, fastest ones hit 10, zero model changes capability is cheap when the scaffolding around it is broken the article breaks down the whole system in 15 minutes ↓

shmidt

36,479 görüntüleme • 3 ay önce

YOUR OBSIDIAN VAULT READS ALL 2,000 NOTES TO ANSWER ONE QUESTION. THAT'S NOT A SECOND BRAIN. That's a hoarder with markdown files. Hundreds of thousands of tokens burned per answer, and it still grabs the wrong note half the time. A real second brain runs on a graph: a router, an index, nodes, edges. Fable 5 reads 2-3 files instead of the whole vault. Answer back in under a second. The graph view everyone screenshots in Obsidian is a poster. The index is the actual engine. Full 11-step build below, from raw vault to working graph 👇

YOUR OBSIDIAN VAULT READS ALL 2,000 NOTES TO ANSWER ONE QUESTION. THAT'S NOT A SECOND BRAIN. That's a hoarder with markdown files. Hundreds of thousands of tokens burned per answer, and it still grabs the wrong note half the time. A real second brain runs on a graph: a router, an index, nodes, edges. Fable 5 reads 2-3 files instead of the whole vault. Answer back in under a second. The graph view everyone screenshots in Obsidian is a poster. The index is the actual engine. Full 11-step build below, from raw vault to working graph 👇

unicode

53,046 görüntüleme • 2 ay önce

This is the one GitHub repo that everyone needs to save. The CEO of Obsidian literally open-sourced his entire Claude Skills vault. It's his personal Obsidian vault template, and it's built to 10x your AI productivity. To get started: 1. Download the vault in the GitHub link below 2. Unzip the .zip file (Claude can help here) 3. Open Obsidian & create a new vault pointing to that folder →

This is the one GitHub repo that everyone needs to save. The CEO of Obsidian literally open-sourced his entire Claude Skills vault. It's his personal Obsidian vault template, and it's built to 10x your AI productivity. To get started: 1. Download the vault in the GitHub link below 2. Unzip the .zip file (Claude can help here) 3. Open Obsidian & create a new vault pointing to that folder →

Miles Deutscher

128,989 görüntüleme • 2 ay önce

holy sh*t. Jev's founder Diogo Almeida dropped a Google Doc on coding agents one of these PDFs has the map everyone needs: where Jev sits in an agent loop ↓ agents do the work, Jev makes the calls, the LLM only writes: 1 → task in: one Noul before anything runs. in scope, or does a human need to see it first? 2 → dispatch: a Choice picks who takes the step (research, write or review). act at 0.85+, everything below goes to review 3 → act: Jev gates the tool call (allow, confirm or block). the LLM only writes the arguments 4 → check: a Score decides what comes back (drop, summary or keep). whatever Jev is unsure of goes to a human 5 → finish: Jev says done above 0.8, then code proves the file actually exists the LLM shows up in 2 of 5 stages. everything else is a typed answer, plain code or a person. don't refactor your agent. hand Jev the one fork it hits most (usually the tool gate or dispatch) and watch 3 numbers for a week: cost per completed task, time per completed task, and how often escalation fired and whether it was right. if escalation never fires, the threshold is too loose and your agent is quietly running unsupervised.

holy sh*t. Jev's founder Diogo Almeida dropped a Google Doc on coding agents one of these PDFs has the map everyone needs: where Jev sits in an agent loop ↓ agents do the work, Jev makes the calls, the LLM only writes: 1 → task in: one Noul before anything runs. in scope, or does a human need to see it first? 2 → dispatch: a Choice picks who takes the step (research, write or review). act at 0.85+, everything below goes to review 3 → act: Jev gates the tool call (allow, confirm or block). the LLM only writes the arguments 4 → check: a Score decides what comes back (drop, summary or keep). whatever Jev is unsure of goes to a human 5 → finish: Jev says done above 0.8, then code proves the file actually exists the LLM shows up in 2 of 5 stages. everything else is a typed answer, plain code or a person. don't refactor your agent. hand Jev the one fork it hits most (usually the tool gate or dispatch) and watch 3 numbers for a week: cost per completed task, time per completed task, and how often escalation fired and whether it was right. if escalation never fires, the threshold is too loose and your agent is quietly running unsupervised.

Archive

33,524 görüntüleme • 16 gün önce

Fable 5 just dropped and someone already built a $10k-looking website with it. Here's the full workflow The smooth scrolling, the animations, the transitions, all built with Fable 5. Here's how to get the same result instead of a generic template. 1. Steal a direction first. Go to Mobbin or Godly and find a site whose feel you love. Screenshot it. Fable copies a vibe far better than it invents one. 2. Grab real components. Pull animation-ready blocks from Aceternity UI or Magic UI. These are built for exactly this look (scroll reveals, parallax, gradients). 3. Prompt Fable with all of it. Switch to Fable 5 in Claude Code, drop in your screenshots and the component references, and describe the sections and scroll behavior you want. Let it plan before it builds. 4. Animate with the right tools. Tell it to use GSAP with ScrollTrigger for scroll effects, or Framer Motion for React. This is what separates "nice" from "$10k." 5. Iterate on feel, not code. Preview it, then give Fable specific notes ("slower fade, more spacing on mobile") until it lands. Bookmark this

Fable 5 just dropped and someone already built a $10k-looking website with it. Here's the full workflow The smooth scrolling, the animations, the transitions, all built with Fable 5. Here's how to get the same result instead of a generic template. 1. Steal a direction first. Go to Mobbin or Godly and find a site whose feel you love. Screenshot it. Fable copies a vibe far better than it invents one. 2. Grab real components. Pull animation-ready blocks from Aceternity UI or Magic UI. These are built for exactly this look (scroll reveals, parallax, gradients). 3. Prompt Fable with all of it. Switch to Fable 5 in Claude Code, drop in your screenshots and the component references, and describe the sections and scroll behavior you want. Let it plan before it builds. 4. Animate with the right tools. Tell it to use GSAP with ScrollTrigger for scroll effects, or Framer Motion for React. This is what separates "nice" from "$10k." 5. Iterate on feel, not code. Preview it, then give Fable specific notes ("slower fade, more spacing on mobile") until it lands. Bookmark this

Fokki

72,175 görüntüleme • 3 ay önce

this is f**king dangerous someone figured out how to make Opus 4.8 run on Fable 5's brain with one prompt access to the best model is never guaranteed. It disappeared once already this year. but you can use it forever. here's how: 1. ask Fable 5: "write the operating manual your replacement will run on" (procedures, failure modes, a 5-question self-test) 2. save the output as one .md file and drop it into a new Claude Project as the project instructions 3. switch to Opus 4.8 and now your everyday model runs off the smart one's method, no top-tier price save and bookmark this no matter what full extraction prompt is in the article below: ↓

this is f**king dangerous someone figured out how to make Opus 4.8 run on Fable 5's brain with one prompt access to the best model is never guaranteed. It disappeared once already this year. but you can use it forever. here's how: 1. ask Fable 5: "write the operating manual your replacement will run on" (procedures, failure modes, a 5-question self-test) 2. save the output as one .md file and drop it into a new Claude Project as the project instructions 3. switch to Opus 4.8 and now your everyday model runs off the smart one's method, no top-tier price save and bookmark this no matter what full extraction prompt is in the article below: ↓

Hamza Khalid

32,581 görüntüleme • 3 ay önce

my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over

my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over

Cody Schneider

79,914 görüntüleme • 5 ay önce

this is f*cking gold. I told Fable 5 it's a retiring engineer on its last week, and its final task is to leave everything behind for the team replacing it. It read my entire git history. Every mistake, every dead end, every fix that finally worked. Then it started writing them into skill files so the cheaper models replacing it never repeat what I got wrong. Fable leaves July 12. What it leaves behind is up to you. (full breakdown in the article below)

this is f*cking gold. I told Fable 5 it's a retiring engineer on its last week, and its final task is to leave everything behind for the team replacing it. It read my entire git history. Every mistake, every dead end, every fix that finally worked. Then it started writing them into skill files so the cheaper models replacing it never repeat what I got wrong. Fable leaves July 12. What it leaves behind is up to you. (full breakdown in the article below)

Prajwal Tomar

125,537 görüntüleme • 3 ay önce

jev + opus 5.5 for design is insane... a launch video used to mean an agency, weeks of back and forth and a fat invoice. now Opus 5.5 makes the video, the ad cuts and the landing page from one brief. the video below: made in 3 minutes. at 0:09 it drops into edit mode - dot grid, selection handles, pixel sizes on every card - so you see exactly how it was built. but most people get the same mid result: centered text, gradient background, everything fading in. That's Opus's default look when it has nothing to copy. here's the pipeline that makes it look like an agency did it: 1. Build the brand brain first → logo, colors, fonts, screenshots of your real product, your best ad copy → have Opus turn it into a DESIGN.md. The video and the site both follow it 2. Steal a reference, don't describe one → pick 1-2 launch videos with the pacing you want → "make it like this" beats "make it modern and punchy" every time 3. Give it a renderer → install HyperFrames or Remotion: every scene is code, rendered straight to mp4 → a change = one line and a re-render, not starting over 4. Use real components → buttons, cards and UI from 21st_dev instead of empty placeholder boxes → fake-looking UI is the fastest way to look cheap 5. Storyboard before motion → 3 storyboard options, you pick one, then one still per scene → fixing a still takes seconds, fixing a render takes a re-render 6. Run it as a 4-person team + jev → breakdown: pull pacing, type and transitions from the reference → storyboard: the 3 options and the stills → build: every scene in code, music, draft render → review: watch it second by second, list the problems, touch nothing → jev: scores every review note against DESIGN.md (fix now / later / skip) in milliseconds, so build only touches what matters 7. Cut the ads from the same project → same scenes, different hooks: pain first, result first, offer first → 9:16 for reels, 1:1 for feed, 16:9 for YouTube. No reshoots 8. Ship the landing page from the same DESIGN.md → the hero is a looping cut of the launch video → before launch: mobile, every button state, zero placeholder text 9. Give notes like a director → "slow this zoom by half", "hard cut here", "push in on the button" → "make it better" gets random changes paste this into Claude Code 👇 "Read my brand folder and write DESIGN.md. Break down the reference video I give you: pacing, type, transitions. Show me 3 storyboards, then one still per scene for the one I pick. Build every scene in HyperFrames, add music, render a draft. Review it second by second without editing and list the problems. Pass every note to Jev and have it score each one against DESIGN.md: fix now / later / skip. Fix only "fix now". After I approve: cut 3 ad variants with different hooks in 9:16, 1:1 and 16:9, then build the landing page from the same DESIGN.md with the video loop as the hero." everyone has the same model. bookmark this and start using the edge.

Archive

39,253 görüntüleme • 14 gün önce

I gained 25,600+ visitors in 6 months. Here's how I achieved this: 1. Go to Google Search Console. 2. Now, select the last 3 months of data. 3. Open any URL from the Pages tab. 4. Download the Excel or CSV file. 5. Open Claude and upload the file. 6. Find missing keywords. 7. Add those keywords to the content. You can also automate it, too. Yeah, by connecting GSC to Claude...

I gained 25,600+ visitors in 6 months. Here's how I achieved this: 1. Go to Google Search Console. 2. Now, select the last 3 months of data. 3. Open any URL from the Pages tab. 4. Download the Excel or CSV file. 5. Open Claude and upload the file. 6. Find missing keywords. 7. Add those keywords to the content. You can also automate it, too. Yeah, by connecting GSC to Claude...

Hridoy Reh

28,937 görüntüleme • 2 ay önce

🚨Gemini 3.6 Flash is trash I tested it on a 3D Golden Gate Bridge, and the results were awful. • I had to re-prompt it three times because it repeatedly ignored the instructions. • First attempt, instead of creating the requested .html file, it first tried to build the experience inside the Gemini app using simulations. • Then second attempt it started placing images from the web into the chat rather than actually producing the file. • Even after getting it to complete the task, the final output was dramatically worse than Gemini 3.1 Pro, which is 5 months old and now not even a top 10 model on leaderboards. This feels like a regression from Gemini 3.5 Flash and honestly, it is one of the weakest models I have tested in the past few months. Has anyone else tested Gemini 3.6 Flash yet, and are you seeing the same thing?

🚨Gemini 3.6 Flash is trash I tested it on a 3D Golden Gate Bridge, and the results were awful. • I had to re-prompt it three times because it repeatedly ignored the instructions. • First attempt, instead of creating the requested .html file, it first tried to build the experience inside the Gemini app using simulations. • Then second attempt it started placing images from the web into the chat rather than actually producing the file. • Even after getting it to complete the task, the final output was dramatically worse than Gemini 3.1 Pro, which is 5 months old and now not even a top 10 model on leaderboards. This feels like a regression from Gemini 3.5 Flash and honestly, it is one of the weakest models I have tested in the past few months. Has anyone else tested Gemini 3.6 Flash yet, and are you seeing the same thing?

Lumina

72,529 görüntüleme • 2 ay önce

I STOPPED REVIEWING MY OWN AGENT, SOMETHING ELSE DOES IT NOW I used to read every diff it produced and approve most of them, because an agent grading itself always says the work is good. -> Now a second model with different instructions tries to break the work first, and I only read what survived. Here is what is actually in the folder that took over the night shift: • the brief > CONTRACT.md -- what it may touch, and what it may never touch. > VISION.md -- the destination, so turn 47 still knows why it started. • the gate > judge/ -- a different model, never the one that wrote the code. > break-it.md -- it opens the page, clicks, screenshots, reports back. > -- no opinion, just zero or non-zero. > shift.yml -- 03:30 every night, laptop closed. • the memory > receipts/ -- one folder per night, dated and graded. > STATE.md -- where it stopped and what it escalated. > lessons.log -- the flaky test, written down once instead of rediscovered weekly. • the brakes > caps.json -- turn limit, retry limit, spend limit. > -- written on day one, used never. The generator decides what your loop can produce -> The judge decides what it refuses to produce. One of those is the part everyone builds -> The other is why most loops quietly fail. Bookmark it & Read Full breakdown below ↓

I STOPPED REVIEWING MY OWN AGENT, SOMETHING ELSE DOES IT NOW I used to read every diff it produced and approve most of them, because an agent grading itself always says the work is good. -> Now a second model with different instructions tries to break the work first, and I only read what survived. Here is what is actually in the folder that took over the night shift: • the brief > CONTRACT.md -- what it may touch, and what it may never touch. > VISION.md -- the destination, so turn 47 still knows why it started. • the gate > judge/ -- a different model, never the one that wrote the code. > break-it.md -- it opens the page, clicks, screenshots, reports back. > -- no opinion, just zero or non-zero. > shift.yml -- 03:30 every night, laptop closed. • the memory > receipts/ -- one folder per night, dated and graded. > STATE.md -- where it stopped and what it escalated. > lessons.log -- the flaky test, written down once instead of rediscovered weekly. • the brakes > caps.json -- turn limit, retry limit, spend limit. > -- written on day one, used never. The generator decides what your loop can produce -> The judge decides what it refuses to produce. One of those is the part everyone builds -> The other is why most loops quietly fail. Bookmark it & Read Full breakdown below ↓

slash1s

37,556 görüntüleme • 2 ay önce

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Alright, now that we know what an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Google AI

22,487 görüntüleme • 10 ay önce

Drove our HW3 car for the first time with this build this morning. The driver monitoring has definitely been relaxed, extremely close to how v14.2.2.5 does it. I usually catch a strike from HW3 when I do my AI4 behaviors lol. In addition, the super smooth path planner noodle from v14 is also here now. I attached a video from this morning as well as the planner noodle from ~2 months ago.

Drove our HW3 car for the first time with this build this morning. The driver monitoring has definitely been relaxed, extremely close to how v14.2.2.5 does it. I usually catch a strike from HW3 when I do my AI4 behaviors lol. In addition, the super smooth path planner noodle from v14 is also here now. I attached a video from this morning as well as the planner noodle from ~2 months ago.

Francis

103,886 görüntüleme • 6 ay önce

xAI quietly dropped 15 official Grok Bot guides and almost nobody is talking about it Every single one written by the team that built Grok Bot Marketing, Sales, Recruiting, Engineering, Support, Legal - one guide per job Pick yours, follow it, ship your first bot today Most people are still guessing how to build one This is worth more than any $500 course on AI agents and it costs nothing Bookmark this, then read how to hire your first Grok Bot in the article below ↓

xAI quietly dropped 15 official Grok Bot guides and almost nobody is talking about it Every single one written by the team that built Grok Bot Marketing, Sales, Recruiting, Engineering, Support, Legal - one guide per job Pick yours, follow it, ship your first bot today Most people are still guessing how to build one This is worth more than any $500 course on AI agents and it costs nothing Bookmark this, then read how to hire your first Grok Bot in the article below ↓

distort

20,059 görüntüleme • 10 gün önce

AMAZON SENIOR DEVELOPER BUILT A CONTEXT PIPELINE THAT DECIDES WHAT THE MODEL EVEN GETS TO SEE Most teams still dump the entire codebase into every prompt and hope it sorts itself out. He ranked every file by relevance to the task instead of how recently it was touched. A router decides how much context each task earns, a typo fix pulls three files, a full rewrite pulls the whole module. Whatever survives gets compressed to the exact lines that actually matter, so nothing bloats the window with dead weight. Token cost per finished task dropped the moment the model stopped reading dead weight just to fix one function. See how the four stages work together below👇

AMAZON SENIOR DEVELOPER BUILT A CONTEXT PIPELINE THAT DECIDES WHAT THE MODEL EVEN GETS TO SEE Most teams still dump the entire codebase into every prompt and hope it sorts itself out. He ranked every file by relevance to the task instead of how recently it was touched. A router decides how much context each task earns, a typo fix pulls three files, a full rewrite pulls the whole module. Whatever survives gets compressed to the exact lines that actually matter, so nothing bloats the window with dead weight. Token cost per finished task dropped the moment the model stopped reading dead weight just to fix one function. See how the four stages work together below👇

wast3

24,029 görüntüleme • 2 ay önce

whoever leaked this has bigger balls than sense Google Research and MIT ran the same agent jobs 260 different ways for Nature last month: they held the prompts, the tools and the compute budget identical and moved nothing but the wiring between the agents, and the same work swung from 70% worse than a single agent to 80.8% better, averaging out at 0.0% i ran my own single agent against the task list first and it cleared 6 of 10 alone, already past the line where a crew starts subtracting this is Graph Engineering, the layer that decides whether a crew is worth 80% more or 70% less, and it installs into the agent you already pay for: - score your solo agent on the real task first: above roughly 45% success that study predicts zero to negative returns from any crew you put around it - under that line, put one supervisor over the fan out: crews with no correction step amplified their own errors to 17.2x the single agent rate, supervised aggregation held it to 4.4x - give every worker one output and let none of them read a peer's draft, so a wrong step reaches the supervisor instead of four other agents - run the comparison again after every model upgrade, because a better model raises your baseline and a higher baseline is what makes a crew stop paying - keep the single agent alive as the control, the only number that says the wiring is earning its calls turns out the shape does not travel: the biggest win came off a finance task under one supervisor and the worst collapse off a planning task with independent agents my position, and it is the arguable one: a crew is a bet on your own diagram, and the model you pick moves that bet less than one arrow does bookmark this, the three moves that draw those arrows before you pay for one extra call are in the post below ↓

whoever leaked this has bigger balls than sense Google Research and MIT ran the same agent jobs 260 different ways for Nature last month: they held the prompts, the tools and the compute budget identical and moved nothing but the wiring between the agents, and the same work swung from 70% worse than a single agent to 80.8% better, averaging out at 0.0% i ran my own single agent against the task list first and it cleared 6 of 10 alone, already past the line where a crew starts subtracting this is Graph Engineering, the layer that decides whether a crew is worth 80% more or 70% less, and it installs into the agent you already pay for: - score your solo agent on the real task first: above roughly 45% success that study predicts zero to negative returns from any crew you put around it - under that line, put one supervisor over the fan out: crews with no correction step amplified their own errors to 17.2x the single agent rate, supervised aggregation held it to 4.4x - give every worker one output and let none of them read a peer's draft, so a wrong step reaches the supervisor instead of four other agents - run the comparison again after every model upgrade, because a better model raises your baseline and a higher baseline is what makes a crew stop paying - keep the single agent alive as the control, the only number that says the wiring is earning its calls turns out the shape does not travel: the biggest win came off a finance task under one supervisor and the worst collapse off a planning task with independent agents my position, and it is the arguable one: a crew is a bet on your own diagram, and the model you pick moves that bet less than one arrow does bookmark this, the three moves that draw those arrows before you pay for one extra call are in the post below ↓

Argona

892,436 görüntüleme • 2 ay önce

you don't need to re-explain your codebase's architecture to your agent every session. most tools stop at telling you what broke. sentrux is a real-time architectural sensor, it watches your codebase as a live treemap and turns file structure and dependencies into one continuous quality score. the loop is simple: codebase > agent scans structure and dependencies > sentrux scores 5 root cause metrics into one signal > agent sees exactly where risk concentrates > next session starts from a live map instead of a blind grep the binary carries zero built-in language knowledge, all 52 languages live in plugin.toml and tags.scm query files, so a new language needs zero rust code. small catch: it only scores the structure, it won't tell you why the cycle happened, that part's still on you. built pure Rust with no runtime dependencies, specifically so it could sit as one binary between an agent and a codebase without adding friction.

you don't need to re-explain your codebase's architecture to your agent every session. most tools stop at telling you what broke. sentrux is a real-time architectural sensor, it watches your codebase as a live treemap and turns file structure and dependencies into one continuous quality score. the loop is simple: codebase > agent scans structure and dependencies > sentrux scores 5 root cause metrics into one signal > agent sees exactly where risk concentrates > next session starts from a live map instead of a blind grep the binary carries zero built-in language knowledge, all 52 languages live in plugin.toml and tags.scm query files, so a new language needs zero rust code. small catch: it only scores the structure, it won't tell you why the cycle happened, that part's still on you. built pure Rust with no runtime dependencies, specifically so it could sit as one binary between an agent and a codebase without adding friction.

Simplifying AI

18,308 görüntüleme • 2 ay önce

Month 1: $0 Month 2: $0 Month 3: $205 Month 4: 50,532 This is what actually happens when you stop overthinking and start a faceless YouTube channel the right way. Most people quit in the first 60 days because they see zero results and assume it doesn’t work. But the ones who push through the first two months with consistency usually see their first real payout in month 3. Here’s the simple system that makes this possible: Step 1: Pick one proven niche (history, facts, motivation, or luxury) and stick to it. Step 2: Use AI to generate 30 video ideas in one sitting so you never run out of content. Step 3: Outsource or use AI to create the videos (script + voice + editing) so you can upload consistently without burning out. Step 4: Upload 3–5 videos per week and stay consistent for 90 days without checking analytics every day. The first two months are almost always quiet. The third month is where most people either quit or finally get paid. If you’re willing to be patient for 90 days, this model still works extremely well in 2026. Watch the full breakdown in the video.

Month 1: $0 Month 2: $0 Month 3: $205 Month 4: 50,532 This is what actually happens when you stop overthinking and start a faceless YouTube channel the right way. Most people quit in the first 60 days because they see zero results and assume it doesn’t work. But the ones who push through the first two months with consistency usually see their first real payout in month 3. Here’s the simple system that makes this possible: Step 1: Pick one proven niche (history, facts, motivation, or luxury) and stick to it. Step 2: Use AI to generate 30 video ideas in one sitting so you never run out of content. Step 3: Outsource or use AI to create the videos (script + voice + editing) so you can upload consistently without burning out. Step 4: Upload 3–5 videos per week and stay consistent for 90 days without checking analytics every day. The first two months are almost always quiet. The third month is where most people either quit or finally get paid. If you’re willing to be patient for 90 days, this model still works extremely well in 2026. Watch the full breakdown in the video.

Frogify

45,823 görüntüleme • 3 ay önce