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So I gave Step 5 Preview a bug it had never seen before, and just watched. Filtered 193 users out of 208, hit export, and the file came back with 6 rows. Whatever happened to be sitting on the current page. That's the bug I handed it. No file...

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Parul Gautam

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49,002 görüntüleme • 5 gün önce •via X (Twitter)

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25 Yorum

Theinfralab profil fotoğrafı
Theinfralab5 gün önce

Fixing the bug is one thing, making sure it stays fixed is another.

Parul Gautam profil fotoğrafı
Parul Gautam5 gün önce

Exactly! A fix is only reliable when it holds up over time.

Nelly; profil fotoğrafı
Nelly;5 gün önce

fixing the root cause instead of patching symptoms is everything

D-Coder profil fotoğrafı
D-Coder5 gün önce

Love seeing @StepFun_ai focus on accuracy, not just generating answers.

codemarch profil fotoğrafı
codemarch5 gün önce

Going beyond the initial request to handle failed downloads is a nice touch.

Rishabh profil fotoğrafı
Rishabh5 gün önce

I’d trust the result more knowing it reran the exact scenario itself.

Gina Acosta profil fotoğrafı
Gina Acosta5 gün önce

the ability to understand the codebase and trace a bug is what stands out here

Manish Kumar Shah profil fotoğrafı
Manish Kumar Shah5 gün önce

This is what makes AI coding tools genuinely useful in real projects.

Atul Kumar profil fotoğrafı
Atul Kumar5 gün önce

AI coding gets much more interesting when verification is part of the process.

EyeingAI profil fotoğrafı
EyeingAI5 gün önce

The full fix-and-test workflow looks really solid.

Ajay Sharma profil fotoğrafı
Ajay Sharma5 gün önce

No hints, just the codebase and the bug report. That’s a real test.

RAVI KUMAR SAHU profil fotoğrafı
RAVI KUMAR SAHU5 gün önce

193 filtered users and 193 exported rows. That’s the kind of result you want.

Jawad Rahman profil fotoğrafı
Jawad Rahman5 gün önce

This is another step toward making AI more accessible.

Roshni profil fotoğrafı
Roshni5 gün önce

This bug hunting test is wild

Pradeep Pandey profil fotoğrafı
Pradeep Pandey5 gün önce

This looks like a game changer

Carlo Makes profil fotoğrafı
Carlo Makes5 gün önce

6 rows out of 15 is the kind of bug i'd only notice after the client opened the file

JORDAN profil fotoğrafı
JORDAN5 gün önce

Thanks for the share Parul

ᗩshhh profil fotoğrafı
ᗩshhh5 gün önce

193 rows after the fix is the proof that counts.

Hasan Rajpoot profil fotoğrafı
Hasan Rajpoot5 gün önce

The 15 tests are a really important part of this story.

Mujeeb Ahmed profil fotoğrafı
Mujeeb Ahmed5 gün önce

I like that it addressed the naming issue too, not just the immediate bug.

Andrew Bolis profil fotoğrafı
Andrew Bolis5 gün önce

Adding bounded retries unprompted is a thoughtful extra.

Utkarsh Sharma profil fotoğrafı
Utkarsh Sharma5 gün önce

Love that it added retry logic without even being asked.

Dhaval Makwana profil fotoğrafı
Dhaval Makwana5 gün önce

The fact that it tested its own fix makes this even more impressive.

Luqman Ali profil fotoğrafı
Luqman Ali5 gün önce

That's Awesome

IamAlam profil fotoğrafı
IamAlam5 gün önce

One prompt from debugging to verification is a pretty solid workflow.

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I set up Gemini Spark to write my own weekly AI news digest for me, and I'm still stuck on how little effort I had to put in. This is the exact prompt I gave it, just once: "Every Tuesday at 12:15 PM, create a new Doc in my Drive, find the 5 biggest AI stories from that week, and format each one properly." And here's what it actually produces, every week: 1. A new Doc, created and named on its own 2. The 5 biggest AI stories from that week, found and picked automatically 3. A bold headline and a summary for each one 4. The key impact spelled out, not just the news 5. A source link on every single story This runs on a schedule so I never have to think about it again. I didn't ask anything on Tuesday, I just opened the drive and the news digest doc was already there. Just like this, you can use Spark to automate any recurring task.

Eyisha Zyer

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0:58

2 hours 43 minutes. 442 steps. 5,406 lines of code. The page still does not load 💀 I gave Qwen3.8 Flash Next my standard bench prompt: a voxel Japanese pagoda garden in Three.js, 16,500 voxels, modular source, run it and screenshot it when it's done. Other models finish this one easy. It never opened a browser. Never started the dev server. Never counted a voxel. It spent the entire run fighting its own export statements. petals.js got rewritten 19 times. Not patched, rewritten from scratch every time. water.js 9 times, palette.js 11, lanterns.js 7. It was loop between exactly 2 errors: Export 'update' is not defined in module. 53 times. Duplicate export of 'createPetals'. 10 times. Add the alias and you get the duplicate. Remove it and the export goes missing. In the last 70 steps it flipped between those 2 states 9 times. Twice it deleted the file and wrote it back identical. 85 writes against 7 reads. It almost never looked at what was already on disk. It compacted its own context 4 times and came back to the same 2 errors every time. It stopped with 3 files still failing node --check, all on the same line, update as updatePetals. 2 modules that main.js imports do not exist at all, and one of them is the lanterns file it had written 7 times. Then I gave the same model the same prompt as a single HTML file. It shipped a complete working scene in 1 pass. So it is not a capability wall. The module code it wrote is decent. It cannot hold its own module graph together, and it has nothing that tells it that it is going in circles. 438,737 output tokens and it never once said it was stuck. I spent 3 days making this thing run on my rig. An FP8 KV cache path that vLLM rejects on Ampere in 4 separate places. The 51B n-gram table baked down to FP8 so it fits in host RAM. W4A16 weights, the full 262K context, on 4 gaming GPUs from 2020. All of that works. And then it cannot wire 16 files together. So I'm going back to Qwen3.8 27B as my daily driver until something changes. Could be something in my own build doing this, I'm rebuilding the quant to find out. Either way I'll post what comes back.

Alexey Fateev

31,282 görüntüleme • 1 ay önce

How to create Farcaster mini app without coding - Step 1: > Go to ChatGPT > Just type: "I would like to create a Farcaster mini app, can you give me some ideas?" > Choose any one of the ideas - Note: - You can also type your own idea to ChatGPT and alter it - Step 2: - Go to ChatGPT > Paste the first prompt (shared in my channel) > Include the project you chose and tell ChatGPT to alter the prompt according to the idea below - Step 3 - Go to: > Sign up using Gmail or GitHub > Projects -> New Project -> Type the name of your project >Just paste the prompt we created earlier > It will start creating your app > If you need to alter anything, just go to GPT and type: [ I need a prompt to alter "your issue (like changing colour, etc.)" in my app already created in v0app by Vercel ] > Copy the prompt and paste it in v0 > No need to worry about errors , it will rectify them for you > If your project is finished, just click "Publish" on your top right > It will automatically host your app in Vercel > Click "Visit site" to see your app on another device and copy the website URL - Step 4 - Go to: v0 app > Just paste Prompt 2 from the file I provided > Click Publish once it’s completed - Step 5 - Go to: v0 app > Just paste the prompt 3 from the file I provided - Step 6 (Important) > Just paste Prompt 4 from the file I provided - Step 7 > Just paste Prompt 4 from the file I provided - Step 8 > Just paste Prompt 6 from the file I provided > It’s an important step to do — you need to create a manifest inside your app so that you can host it on Farcaster - Step 9 - Go to: > Sign up or Login > Settings → Developers → Mini Apps → Create Manifest (New) > Paste your website URL (remove https) > It should be like: > If you find any error while doing this, just copy and paste it in the v0 app > Once everything is finished, you need to create Account Association > Scan the QR on your mobile and tap and hold the Yes button > It will show an error — just copy and paste those errors into the v0 app > Click Publish again, and then click Refresh in Manifest > Click Open your app > for the main prompt file check our tg channel link in our bio
33:22

How to create Farcaster mini app without coding - Step 1: > Go to ChatGPT > Just type: "I would like to create a Farcaster mini app, can you give me some ideas?" > Choose any one of the ideas - Note: - You can also type your own idea to ChatGPT and alter it - Step 2: - Go to ChatGPT > Paste the first prompt (shared in my channel) > Include the project you chose and tell ChatGPT to alter the prompt according to the idea below - Step 3 - Go to: > Sign up using Gmail or GitHub > Projects -> New Project -> Type the name of your project >Just paste the prompt we created earlier > It will start creating your app > If you need to alter anything, just go to GPT and type: [ I need a prompt to alter "your issue (like changing colour, etc.)" in my app already created in v0app by Vercel ] > Copy the prompt and paste it in v0 > No need to worry about errors , it will rectify them for you > If your project is finished, just click "Publish" on your top right > It will automatically host your app in Vercel > Click "Visit site" to see your app on another device and copy the website URL - Step 4 - Go to: v0 app > Just paste Prompt 2 from the file I provided > Click Publish once it’s completed - Step 5 - Go to: v0 app > Just paste the prompt 3 from the file I provided - Step 6 (Important) > Just paste Prompt 4 from the file I provided - Step 7 > Just paste Prompt 4 from the file I provided - Step 8 > Just paste Prompt 6 from the file I provided > It’s an important step to do — you need to create a manifest inside your app so that you can host it on Farcaster - Step 9 - Go to: > Sign up or Login > Settings → Developers → Mini Apps → Create Manifest (New) > Paste your website URL (remove https) > It should be like: > If you find any error while doing this, just copy and paste it in the v0 app > Once everything is finished, you need to create Account Association > Scan the QR on your mobile and tap and hold the Yes button > It will show an error — just copy and paste those errors into the v0 app > Click Publish again, and then click Refresh in Manifest > Click Open your app > for the main prompt file check our tg channel link in our bio

Maran

48,427 görüntüleme • 10 ay önce

You don't need to know how to code to turn a messy spreadsheet into something you can actually use. I had an Excel file of 20 brands I want to pitch. I dropped it in a folder, opened Qoder, and typed one sentence: turn this into a dashboard with search. Then I watched. It read the file, made a plan, wrote the code, opened the page in the browser, and tested the search on its own. I added a /goal so it knew when it was done: every brand shows, search works. The model: Qwen3.8-Flash, free through September 30. Zero Credits for this whole run. Want to try it? Enter my code BPU7C6 at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.
1:04

You don't need to know how to code to turn a messy spreadsheet into something you can actually use. I had an Excel file of 20 brands I want to pitch. I dropped it in a folder, opened Qoder, and typed one sentence: turn this into a dashboard with search. Then I watched. It read the file, made a plan, wrote the code, opened the page in the browser, and tested the search on its own. I added a /goal so it knew when it was done: every brand shows, search works. The model: Qwen3.8-Flash, free through September 30. Zero Credits for this whole run. Want to try it? Enter my code BPU7C6 at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.

Jay

143,443 görüntüleme • 20 gün önce

I gave Grok Bot $27 and told it: "make money or I'll delete you" result: $27 → $1,084 in just over 25 hours and it's still running. I haven't opened the dashboard since yesterday. it lives on a server in the cloud and trades memes on its own while I sleep I keep waiting for the moment this thing falls apart. it just hasn't happened yet every moment, it: - pulls every new token launch and analyzes liquidity before the chart even loads - runs a rug check, mint authority, LP locks, top-holder concentration, and filters out anything that looks sketchy - analyzes social flow and the mempool to figure out what's actually about to move - only buys when the setup is clean enough to justify the slippage - sizes the next entry based on how the previous flip closed instead of using a fixed amount - swaps in and out on its own, without asking for my confirmation, and skims its own hosting fee off the top first first night it almost died, the balance dropped to $3 before it adapted. then it stopped chasing every shiny new launch, tightened the filters, and the curve finally started going up the whole setup took one evening: described the task, watched it execute a single swap, put it on a schedule, and connected a wallet no server to babysit. no keys to rotate. no code sitting there waiting to break there are quietly more systems like this than anyone is willing to admit, usually you only find out one exists when someone accidentally slips and posts a screenshot of the dashboard save this post now so you have the timestamp for when it finally clicks for you follow me if you want to replicate what I'm doing. I'll be sharing more alpha
0:21

I gave Grok Bot $27 and told it: "make money or I'll delete you" result: $27 → $1,084 in just over 25 hours and it's still running. I haven't opened the dashboard since yesterday. it lives on a server in the cloud and trades memes on its own while I sleep I keep waiting for the moment this thing falls apart. it just hasn't happened yet every moment, it: - pulls every new token launch and analyzes liquidity before the chart even loads - runs a rug check, mint authority, LP locks, top-holder concentration, and filters out anything that looks sketchy - analyzes social flow and the mempool to figure out what's actually about to move - only buys when the setup is clean enough to justify the slippage - sizes the next entry based on how the previous flip closed instead of using a fixed amount - swaps in and out on its own, without asking for my confirmation, and skims its own hosting fee off the top first first night it almost died, the balance dropped to $3 before it adapted. then it stopped chasing every shiny new launch, tightened the filters, and the curve finally started going up the whole setup took one evening: described the task, watched it execute a single swap, put it on a schedule, and connected a wallet no server to babysit. no keys to rotate. no code sitting there waiting to break there are quietly more systems like this than anyone is willing to admit, usually you only find out one exists when someone accidentally slips and posts a screenshot of the dashboard save this post now so you have the timestamp for when it finally clicks for you follow me if you want to replicate what I'm doing. I'll be sharing more alpha

cristal💎

60,024 görüntüleme • 1 ay önce

OPUS 5.5 JUST DROPPED AND I PLUGGED IT INTO NERVE WITHIN AN HOUR. IT SELF VERIFIED ITS OWN TRADE THESIS THREE TIMES BEFORE FIRING. +5 ETH IN ONE SESSION. repo: every model before this proposed trades. opus 5.5 argues with itself before proposing. i replaced the analyst slot. same impulse. same spine. same jev for speed scoring. opus 5.5 now sits where astra used to sit but it does something astra never did. it checks its own work. first pool comes in. opus reads the narrative brief from grok. reads the jev score. reads the analyzer output. writes a thesis. then it attacks its own thesis. finds a weakness. rewrites. attacks again. three rounds. all inside one call. the impulse that exits the analyst slot is not a first impression. it is a thesis that survived its own author trying to kill it. astra wrote one paragraph and moved on. opus 5.5 writes three drafts and only sends the one it could not break. 1M context means it holds the entire narrative map, the last 200 pool results, the ghost log, and the current portfolio in one pass. no truncation. no summary. the full picture. first session i watched it reject a pool that jev scored 0.87. opus wrote the thesis, attacked it, found that the narrative was 14 hours old and decelerating despite fresh volume. the crowd was leaving not arriving. jev saw the numbers. opus saw the story behind the numbers was ending. then it cleared two pools in a row that grok flagged as early narrative. opus confirmed both, self verified, fired. pool 1: +2.1 ETH. pool 2: +2.9 ETH. +5 ETH. one session. the model that argues with itself before spending your money. jev decides in 190ms. opus thinks for 4 seconds. nerve runs both because speed and depth are not the same question.
0:29

OPUS 5.5 JUST DROPPED AND I PLUGGED IT INTO NERVE WITHIN AN HOUR. IT SELF VERIFIED ITS OWN TRADE THESIS THREE TIMES BEFORE FIRING. +5 ETH IN ONE SESSION. repo: every model before this proposed trades. opus 5.5 argues with itself before proposing. i replaced the analyst slot. same impulse. same spine. same jev for speed scoring. opus 5.5 now sits where astra used to sit but it does something astra never did. it checks its own work. first pool comes in. opus reads the narrative brief from grok. reads the jev score. reads the analyzer output. writes a thesis. then it attacks its own thesis. finds a weakness. rewrites. attacks again. three rounds. all inside one call. the impulse that exits the analyst slot is not a first impression. it is a thesis that survived its own author trying to kill it. astra wrote one paragraph and moved on. opus 5.5 writes three drafts and only sends the one it could not break. 1M context means it holds the entire narrative map, the last 200 pool results, the ghost log, and the current portfolio in one pass. no truncation. no summary. the full picture. first session i watched it reject a pool that jev scored 0.87. opus wrote the thesis, attacked it, found that the narrative was 14 hours old and decelerating despite fresh volume. the crowd was leaving not arriving. jev saw the numbers. opus saw the story behind the numbers was ending. then it cleared two pools in a row that grok flagged as early narrative. opus confirmed both, self verified, fired. pool 1: +2.1 ETH. pool 2: +2.9 ETH. +5 ETH. one session. the model that argues with itself before spending your money. jev decides in 190ms. opus thinks for 4 seconds. nerve runs both because speed and depth are not the same question.

h100envy

11,823 görüntüleme • 21 gün önce

New skill: self-managed-context (make the agent's context an editable file) It explains how to build agents that decide what to keep, update, or remove from the information they use to do their work. It can archive a long log while keeping the exact error, update its progress notes, or remove outdated information. Those edits then change what the model sees on its next turn. 1- Keep the system instructions and original task protected, outside the editable file. 2- Write the remaining conversation to a file, with labels for each message. 3- Let the agent edit that file using its usual code tools. 4- After each command, read the file back and use the updated messages for the next model call. Loading the skill ( alone into a fixed harness won't create live context editing, but it can help an agent build and then operate a harness that supports it. I gave the skill to a coding agent and had it build the harness itself. The task is a long stream of server logs that doesn't fit in the window. The agent reads it in 18 chunks, about 10k tokens in total, with a 5.5k budget. It has to report one incident ticket exactly and the final value of every config key. Same model & budget, three setups: 1- Model manages its own context 2- Harness forces a summary at 75% full 3- Keeps everything The video shows a real GPT-5.4 run. - Self-managed solved it 3 out of 3. - Keep-everything overflowed 3 out of 3. - Forced summary also solved it 3 out of 3. On GPT-5.4 the self-managed agent re-processed about 24% fewer prompt tokens than the forced summary. When it edited, it cut hard, so little was left after the edit to re-process (one edit took 5,537 tokens down to 771). On GPT-4.1 it saved nothing. It edited near the top of its context but kept most of what was below, and every edit forces everything after it to be re-processed. This is a small test at about 2x context pressure. The paper goes up to 24x, but imho the video below and the skill are a good way to start understanding the technique.
0:31

New skill: self-managed-context (make the agent's context an editable file) It explains how to build agents that decide what to keep, update, or remove from the information they use to do their work. It can archive a long log while keeping the exact error, update its progress notes, or remove outdated information. Those edits then change what the model sees on its next turn. 1- Keep the system instructions and original task protected, outside the editable file. 2- Write the remaining conversation to a file, with labels for each message. 3- Let the agent edit that file using its usual code tools. 4- After each command, read the file back and use the updated messages for the next model call. Loading the skill ( alone into a fixed harness won't create live context editing, but it can help an agent build and then operate a harness that supports it. I gave the skill to a coding agent and had it build the harness itself. The task is a long stream of server logs that doesn't fit in the window. The agent reads it in 18 chunks, about 10k tokens in total, with a 5.5k budget. It has to report one incident ticket exactly and the final value of every config key. Same model & budget, three setups: 1- Model manages its own context 2- Harness forces a summary at 75% full 3- Keeps everything The video shows a real GPT-5.4 run. - Self-managed solved it 3 out of 3. - Keep-everything overflowed 3 out of 3. - Forced summary also solved it 3 out of 3. On GPT-5.4 the self-managed agent re-processed about 24% fewer prompt tokens than the forced summary. When it edited, it cut hard, so little was left after the edit to re-process (one edit took 5,537 tokens down to 771). On GPT-4.1 it saved nothing. It edited near the top of its context but kept most of what was below, and every edit forces everything after it to be re-processed. This is a small test at about 2x context pressure. The paper goes up to 24x, but imho the video below and the skill are a good way to start understanding the technique.

Muratcan Koylan

15,070 görüntüleme • 13 gün önce

I gave Elon's Grok Bot $60 and one rule: "The day you cannot pay your own subscription is the day I unplug you". 48 hours in it is sitting on $4,412 and it already paid the bill. Autonomous trading agent on Polymarket, running on its own cloud box with its own browser and terminal. I did not write a line of code and I did not watch it trade. Every 15 minutes it: -> Pulls every market that resolves inside 24 hours. -> Reads live X sentiment, and it is Grok, so it sees every post on this app before the price moves. -> Prices each market itself and only flags a gap above 7%. -> Sizes on the gap, never above 5% of the bankroll. -> Executes in its own browser, nobody approves anything. -> Covers its own $200 subscription straight out of profits. Balance hits $0, subscription gets cancelled, it is gone for good. Hour 11 it was at $9 -> runway one day. It dumped every politics position and switched to same day baseball totals, front-running lineup cards before the market repriced. It rewrote its own strategy to not die. Setup was one evening: spin up the bot, run one trade in front of it, put it on a timer, connect Telegram with no VPS, no API keys and no dev on call. Last spring a freelancer took $2,500 for something like this -> it made $410 and died on a rate limit. Grok Bot did in one evening what $2,500 could not. $60 -> $4,412 in 48 hours. What is it sitting at by Sunday?
0:56

I gave Elon's Grok Bot $60 and one rule: "The day you cannot pay your own subscription is the day I unplug you". 48 hours in it is sitting on $4,412 and it already paid the bill. Autonomous trading agent on Polymarket, running on its own cloud box with its own browser and terminal. I did not write a line of code and I did not watch it trade. Every 15 minutes it: -> Pulls every market that resolves inside 24 hours. -> Reads live X sentiment, and it is Grok, so it sees every post on this app before the price moves. -> Prices each market itself and only flags a gap above 7%. -> Sizes on the gap, never above 5% of the bankroll. -> Executes in its own browser, nobody approves anything. -> Covers its own $200 subscription straight out of profits. Balance hits $0, subscription gets cancelled, it is gone for good. Hour 11 it was at $9 -> runway one day. It dumped every politics position and switched to same day baseball totals, front-running lineup cards before the market repriced. It rewrote its own strategy to not die. Setup was one evening: spin up the bot, run one trade in front of it, put it on a timer, connect Telegram with no VPS, no API keys and no dev on call. Last spring a freelancer took $2,500 for something like this -> it made $410 and died on a rate limit. Grok Bot did in one evening what $2,500 could not. $60 -> $4,412 in 48 hours. What is it sitting at by Sunday?

slash1s

190,860 görüntüleme • 1 ay önce

everyone in iOS development should watch this. seriously, it might change the whole industry. i pointed claude code at a live ios device running on revyl, typed "test everything," and walked away. here's what's actually happening: ① you don't write the tests. no scripts, no selectors, no test plan. i never told it which screens to open or what to check. it read the app, decided what mattered, and tested it. the entire instruction was "test everything." ② it built its own test team. it looked at the app, clocked that it's basically four mini apps (rides, delivery, services, account), and split itself into 4 agents, one per surface. scoping coverage like that is usually a person's whole afternoon. it did it in seconds, unprompted. ③ all four ran at the same time, each on its own live device. this is where revyl comes in. every agent gets its own live ios session in the cloud, so four running apps get tested in parallel instead of taking turns on one simulator. serial testing turns coverage into a time tax. running all of it at once removes the tax. ④ it tests like a person, not like a script. each agent drives the app the way a user would, taps through the flows, and visually checks each screen against what it expected to see. nothing is pinned to a brittle element id, so renaming a button doesn't take down half your suite. that one detail is the most annoying thing about how we test today, and it just quietly goes away. ⑤ no xcuitest, no sims melting your laptop. i didn't write a single xcuitest script, and there were no simulators booting on my machine. the agents run on cloud devices, so coverage stops being capped by what your laptop can handle. the part that got me isn't that an agent tested an app. it's that i never told it how. i handed it a device and an intent, and it figured out the scoping, the parallelizing, and the driving on its own. if you still write and maintain mobile ui tests by hand, i'm not sure that lasts the year.
0:47

everyone in iOS development should watch this. seriously, it might change the whole industry. i pointed claude code at a live ios device running on revyl, typed "test everything," and walked away. here's what's actually happening: ① you don't write the tests. no scripts, no selectors, no test plan. i never told it which screens to open or what to check. it read the app, decided what mattered, and tested it. the entire instruction was "test everything." ② it built its own test team. it looked at the app, clocked that it's basically four mini apps (rides, delivery, services, account), and split itself into 4 agents, one per surface. scoping coverage like that is usually a person's whole afternoon. it did it in seconds, unprompted. ③ all four ran at the same time, each on its own live device. this is where revyl comes in. every agent gets its own live ios session in the cloud, so four running apps get tested in parallel instead of taking turns on one simulator. serial testing turns coverage into a time tax. running all of it at once removes the tax. ④ it tests like a person, not like a script. each agent drives the app the way a user would, taps through the flows, and visually checks each screen against what it expected to see. nothing is pinned to a brittle element id, so renaming a button doesn't take down half your suite. that one detail is the most annoying thing about how we test today, and it just quietly goes away. ⑤ no xcuitest, no sims melting your laptop. i didn't write a single xcuitest script, and there were no simulators booting on my machine. the agents run on cloud devices, so coverage stops being capped by what your laptop can handle. the part that got me isn't that an agent tested an app. it's that i never told it how. i handed it a device and an intent, and it figured out the scoping, the parallelizing, and the driving on its own. if you still write and maintain mobile ui tests by hand, i'm not sure that lasts the year.

Landseer Enga

23,963 görüntüleme • 4 ay önce

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,643 görüntüleme • 4 ay önce