Alpha Batcher

> built the app with Claude > shipped to App Store > told X it was live > got downloads > opened the dashboard > nothing > checked the code > StoreKit looked fine > checked App Store Connect > Paid Apps Agreement not active > banking missing > tax forms unfinished > Small Business Program never applied for > Apple still taking 30% > first payout still 45 days after fiscal month close > external payment copy sitting in one forgotten settings screen > reviewer would have found it instantly The app was not broken, the business setup was before you launch an iOS app, do this: > set up banking > complete tax forms > sign Paid Apps Agreement > apply for Small Business Program if under $1M > create real product IDs > test sandbox purchase > test restore > test cancellation > test subscription grace period > remove every sloppy external payment reference > then submit vibe coding gets you the app App Store Connect gets you paid

@Blum_OG more memory made them more confident but less accurate

> we gave agents more memory > they got more confident > but less accurate Andrej Karpathy: "you rarely ever write or edit the wiki manually; it's the domain of the LLM" most company AI stalls after 3 weeks for the same reason calls sit in Gong. SOPs sit in Notion corrections sit in someone's head the agent wakes up stupid every morning here are the 6 layers that fix this: 1. capture is not the finish line > calls and SOPs are raw material, not intelligence 2. retrieval beats memory > the agent doesn't need 500K tokens > it needs the 6 facts relevant to this task 3. source hierarchy is a design decision > when the Slack note conflicts with the SOP > the system needs a rule, not a coin flip 4. permissions are not optional > marketing doesn't need client financials > build the walls before you need them 5. corrections are data > if a human fixed the same thing twice > it should become a rule, not a habit 6. feedback loops make it compound > without them you're babysitting software > with them every fix trains the whole system Single Grain ran 2,862 Gong calls through this one daily run: 15 calls became 390 insights, 470 facts, 125 frameworks one call became: > an objection library > a sales training input > a CRM risk flag > a future agent instruction the winners aren't building bigger prompt libraries they're building cleaner intelligence layers

> spend $1,900/mo renting cloud GPUs > call it infrastructure > run client fine-tunes > host a 70B assistant > process private document batches > forget one instance overnight > watch the invoice eat the job then notice the math: > DGX Spark: $4,699 today on NVIDIA Marketplace > 128GB unified memory > 4TB NVMe > GB10 Grace Blackwell > Ollama on `localhost:11434` > Open WebUI on `localhost:3000` > OpenAI clients pointed at `base_url=http://localhost:11434/v1` It can replace rented GPU hours for models you run every week Check this before buying: > can the model fit > can the workload run locally > is the client data sensitive > is your monthly GPU bill above $1,000 > do you need memory capacity more than raw speed If yes, price the box against 3 months of invoices If no, use the cloud and keep moving

AI is making kids dumber. It should be making them geniuses. Introducing Koji, the first AI tutor that gets kids to actually think. 👇

@suekhim How does Koji handle the moment when a kid has been struggling for 20 minutes and just wants the answer?

@HarryTandy clean take. the real shift is less about code generation and more about repo discipline

Andrej Karpathy: "Neural networks are not just another classifier, they represent the beginning of a fundamental shift in how we write software" That was 2017 Now the shift is inside the repo Claude can write code fast. The hard part is making it respect the codebase Here are the 10 rules senior engineers keep in their AI setup: 1. Read before editing. Callers, exports, shared utilities, tests. No blind patches 2. Touch only what the task requires. Adjacent cleanup is how small fixes become messy PRs 3. State assumptions before coding. The model cannot read intent from silence 4. Define success upfront. A vague task gets a vague finish 5. Match existing conventions. Do not silently move class components to hooks. Do not invent a new test style 6. Surface conflicts. Two patterns in the codebase? Pick one and explain why 7. Write tests that can fail for the right reason. A test that passes when logic is broken is decoration. 8. Run checks after meaningful edits. Format, lint, typecheck, test 9. Fail loud. Skipped records, partial migrations, uncertain outputs - all visible 10. Save repeated mistakes into CLAUDE.md. The repo should remember what the chat forgets This is where teams get leverage from AI coding Not from longer prompts From shorter feedback loops, memory and making the model prove it finished

@HarryTandy one job per agent, read only review, and a hard stop on ambiguity

AI AGENTS AREN’T “WRITE ME A POST” ANYMORE The mistake most people make: they spin up one bot and expect it to act as strategist, builder, tester, and editor at the same time It falls apart fast I broke down a real 4-agent workflow Here are the 12 parts worth stealing 1. A single agent pollutes its own context fast. Planning, code, tests, fixes, explanations - all dumped into the same thread. By message 30, it’s fighting a task it misunderstood at message 3 2. Agent teams work better when every agent has one job. Planner thinks. Coder builds. Tester tries to break it. Reviewer decides if it ships 3. The key is the handoff file. Not vibes. Not “the next agent gets the idea.” A real file: spec.md, changes.md, test-results.md, review.md. If the next agent has nothing concrete to read, the pipeline is dead 4. Planner should never touch code. Its job is to kill ambiguity. What files change. What edge cases matter. What repo patterns to copy 5. Coder should not “clean up nearby stuff.” It reads spec.md and builds exactly that. The second it starts being helpful, your tiny task becomes a 900-line PR 6. Tester doesn’t patch the code. It writes tests, runs them, and stops the pipeline when something breaks. Otherwise, you just created another coder with worse context 7. Reviewer should be read-only. Give it edit access and it will start covering up problems instead of calling them out 8. Green tests are worthless if the tests check the wrong thing. A real test breaks when the business logic breaks. Everything else is theater 9. The most expensive failure is a quiet one. “Completed successfully” while skipping 14% of records is worse than a failing test 10. Don’t start with 20 agents. Start with two: Planner -> Coder. Once that handoff works, add Tester. Then Reviewer 11. An AI team without rules becomes noise fast. Set hard limits: no nearby refactors, no invented requirements, no moving forward with open questions 12. Speed comes from discipline. Handoffs first. Tests second. Overnight runs third. Flip that order and you get chaos What actually compounds: > one rules file per repo > short handoff docs > a checkpoint after every stage > read-only review > no “helpful” side quests > a hard stop on ambiguity AI agents don’t replace engineering discipline They expose the lack of it faster than any human would Save this if you’re building an actual pipeline, not just chatting with a bot

@w1nklerr NVIDIA + AI = money good combine for work

@RetroChainer if be honest, NVIDIA like a ecosystem so popular now happy to know it

> cloud GPU pricing teaches one bad habit: rationing > you think twice before looping an agent > before re-running the whole archive > before fine-tuning on a hunch > that hesitation is where the money hid > a $2,999 NVIDIA DGX Spark kills the meter > 70B running local, ~$10/mo in power > the quiet killer feature: nothing leaves your desk > contracts, patient records, NDA work stays local > clients ask where their data goes now. local wins deals

@gippp69 i want to have a couple NVIDIA boex for myself

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

@gippp69 > be Kimi user > be rich easy plan asf

THIS $500/MO KIMI STACK JUST REPLACED A 12-PERSON DEV TEAM they plugged Kimi K2.6 into this old iMac and launched 300 agents from the terminal 4,000 steps later, it was researching, coding, testing, writing docs, and building client systems in parallel a normal agency charges $10,000 for this and loses half of it to salaries here, the expensive part becomes API spend, review time, and one person who actually understands what the client wants the screen looks like it should be running old iTunes but the workflow is closer to an $80,000/month agency than a chatbot

@kimmonismus Epoch AI is good tool bro, but still underrated

According to research by EpochAI, open-weight models lag behind frontier closed-source models by four months. Four months. That's very little. And impressive at the same time.

@coreyganim time to have my AI assessment offer

A guy offered me $1,000 to follow him around for a day and tell him where to implement AI in his business. I turned that idea into my AI Assessment offer. Full blueprint below (steal this):

@kimmonismus NVIDIA working without stopping more eto come

NVIDIA is moving all four open model families - Cosmos, Isaac GR00T, Ising, Nemotron - onto the Linux Foundation's OpenMDW-1.1. Right now open-weight models come with a patchwork of software licenses that were never meant for AI plus bespoke terms with usage limits, so anyone serious has to run legal on each one before they build. OpenMDW puts weights, code, docs and data under a single permissive license, so that becomes one review instead of ten. It makes a lot of sense -for NVIDIA in particular. Their business is mainly on compute, so getting more people to deploy and fine-tune open weights is a very good business case.

@TechByTaraa for sure everyone prefer to Google, easy

As a developer, which browser do you prefer using?

@Akasheth_ Haiku is still alive?

Anthropic should release a new haiku model

@0xwhrrari well, Anthropic team always give us free guides

Claude opus 4.8 dropped and Anthropic released Boris Cherny's prompt workshop One of the people actually building claude explains how to use it properly Free No signup No paywall The first 8 minutes are better than most $300 ai courses Context Task structure Cleaner outputs Fewer wasted prompts Watch it and bookmark it before this gets sold back to you as a course

@Akasheth_ you working with Copilot bro? i'm not

Bro disappeared like he never existed

@OpenAI me like a windows user so happy big thanks for OpenAI

Windows users, this one’s for you. Computer use now works on Windows, so Codex can take action on your Windows computer. And with Windows support for Codex in the ChatGPT mobile app, you can start, review, and steer tasks on the go while work continues on your Windows machine. An early experience, but we’re working on more ways to keep your work moving, wherever you are.

@Sarthak4Alpha i don't want to be in 2027 haha

AI career progression according to the internet. 😂

@Bober_smart this waifus with AI looks cute 😂

50-year-old woman from Shanghai decided to just make a joke but in the end she earned $3958 in 4 hours With the help of Claude, she created a Chinese girl and launched her first stream Claude changed her appearance, background, voice and even language in real time She launched the stream simultaneously on 3 platforms: YouTube, TikTok and Twitch Men liked the girl so much that she started receiving donations after just 15 minutes > In 4 hours she was donated $3958 > The biggest donation was $550 The article contains 10 folders with tools for creation and workflow automation