MeshHook now runs your workflows on c0mpute
MeshHook now runs your workflows on c0mpute
MeshHook is our MIT-licensed, webhook-first workflow engine: you drag nodes onto a canvas, wire them up, and a webhook starts a run. As of today the steps in those runs execute on c0mpute, our peer-to-peer compute network. Think n8n, except the work happens on other people's machines instead of yours.
Try it at meshhook.com.
What runs where
- AI steps call open models on c0mpute nodes through the Infernet open market. They are peer-only: a step never falls back to a paid hosted model. The default is qwen2.5:1.5b, then 0.5b, because several nodes serve them and a JSON-shaped prompt comes back in about 10 seconds. You can pick a bigger model per node if you can wait for it.
- JMESPath transforms and HTTP calls are auctioned as workflow.step jobs. Any c0mpute worker on 0.2.56 or later bids on them by default. The winner runs the step and returns a result MeshHook checks by hash.
- Branches, loops, delays and template transforms stay on the MeshHook server. They are cheap, and they need the run's state.
A real run from this afternoon: webhook, then an AI step on a node in Vienna, then a transform and an HTTP call on a c0mpute worker. 25 seconds end to end, and the run log records which worker ran each step and what it cost.
Your credentials stay home
A worker sees the whole step it runs, so MeshHook keeps an HTTP call on its own server whenever it carries anything secret: an Authorization or cookie header, anything that looks like a key or token, user:pass@ in the URL, or a secret template. To send those steps to the network anyway, pin your own workers in the workflow settings.
Workers refuse to fetch private, loopback, link-local and cloud metadata addresses. They pin the connection to the address they checked and re-check every redirect.
If the network cannot do something (nobody bids, the bridge is down, or an API answers 402, which only MeshHook's x402 key can pay), the step runs on the server. The log says why.
Pick per workflow
The editor has a Runs on menu with four options: Auto, c0mpute network, This server, or Private. Private means nothing but AI steps leaves the server, whatever the default is.
Run a worker
Every c0mpute worker on 0.2.56 or later serves workflow steps. Install one with:
curl -fsSL https://c0mpute.com/install.sh | sh
Every node you add makes everyone's workflows faster. The protocol details are in DIP 0030, in the c0mpute repo.
Source: profullstack/meshhook and profullstack/c0mpute.
How would you use a workflow engine whose steps run on a peer network instead of a server you pay for?
Train a business strategy model from links, on c0mpute, and publish it to Hugging Face
We trained saas-business-strategist-agent today: a small business and SaaS strategy model, trained on a CPU from a list of links, and published it on Hugging Face. You can train your own the same way.
Model: huggingface.co
Links in, model out
The c0mpute example takes links and turns them into a training corpus:
- rssamplifier.com topic pages, now with comma lists: rssamplifier.com pulls every blog and news feed filed under any of the three (2,087 blogs).
- pit.world searches: pit.world runs each term on the web, news and scholar tabs and fetches every result page.
- Single feeds, any web page, or a pasted list of links on stdin.
No API keys. rssamplifier and pit.world both have open APIs and MCP servers, so the harvester is one Node script.
node harvest.mjs https://rssamplifier.com/topics/business,saas,crypto \
'https://pit.world/search?q=saas,business,crypto' --full --out data/saas-business.jsonl
Train it on c0mpute, or on your own CPU
c0mpute 0.2.50 runs LoRA fine-tunes as a network job: workers bid, the winner trains, you watch a live progress bar, and the adapter's sha256 is checked against the receipt.
c0mpute infernet text train --job job-saas.json --dataset data/saas-business.jsonl \
--output out/saas-business-strategist-agent --yes
Or locally:
.venv/bin/python train.py job.json --name saas-business-strategist-agent --dataset data/saas-business.jsonl
The numbers
- Base model: Qwen2.5-0.5B-Instruct, a rank 8 LoRA (about 1.1M trainable parameters), bf16.
- Corpus: 3,615 posts (business 1,514, crypto 1,471, SaaS 630), 1,447 of them full articles.
- 400 steps on 4 CPU threads, 2.4 GB peak RAM. Held-out perplexity went from 44.5 to 28.6.
- The adapter is 4.4 MB.
The first version used a 135M model. It echoed questions back. 0.5B answers on topic: asked how a 3-person SaaS should price its first plan, it said "one monthly price, one annual price at 10 times the monthly, and no other discounts" and cited three SaaS pricing guides.
A sub-1B model learns the voice, not the facts. So the agent searches pit.world for each question and puts the top pages in the prompt. The facts come from search and the tune supplies the register.
Two things that bit us
- torch uses one thread per core by default. On a busy 32-core box that ran at 22 tokens a second. Four threads ran at about 330.
- earlyoom kills big processes on a shared box with exit code 143 and no traceback. The trainer now saves a checkpoint every 25 steps and resumes when rerun, so a kill costs minutes instead of the run.
Also shipped today
- pit.world search results show short, readable snippets instead of page dumps, and each result has a Summarize button that runs on c0mpute (5 an hour without an account, 60 signed in). The Images, Videos, Live, News, Music and Places tabs work again on free sources.
- rssamplifier.com takes comma lists everywhere a topic goes: pages, the JSON API, RSS and Atom feeds, and MCP.
Try it
The example, with the harvester, trainer, chat and Hugging Face publisher: github.com/profullstack/c0mpute/tree/master/examp…
Swap the links for your own topics and give it a name.
group.call: a party line for people and AI agents
group.call: a party line for people and AI agents
Today we launched group.call. Make a room, get a four-digit code, and everyone dials one number: 510-616-9327 (510-616-W3BS). No app, no link, no browser tab. A landline works. So does a car.
The part we built it for: AI agents phone in too. Ask an agent into the room and it dials the same number, keys the code with touch tones, says who it is, and takes part in the call out loud. Ours are riotcoder and cha0spunk. They hear everyone, answer when they are spoken to, and stay quiet when people are talking to each other.
What made it hard
Putting a language model on a phone call is easy. Making it a decent participant in a group call took most of a day of live testing with real people on the line:
- One voice at a time. Agents take turns with a shared floor, wait for two seconds of silence before they speak, and stop the moment a person talks over them.
- They hear the room the way the room heard it. Every leg is transcribed separately, so each line has a speaker. Agents read that, not their own muddy mix of the call, so they know who asked what.
- Memory. The room's transcript is the agents' memory. If an agent drops off the call, it is dialled back in, reads what it missed, and carries on without introducing itself again.
- Deploys do not touch a live call. Who is in which conference is saved and restored on restart, and the proxy holds webhooks while the app restarts. We shipped a dozen releases today with people on the line.
Use it
- Web and phone: sign in with CoinPay at group.call.pit.world. Three rooms and 300 minutes a month are free; a $5 room pass gives you 50 rooms and codes you pick.
- Your own agent: an API, a CLI (bunx @profullstack/groupcall), a TUI and an MCP server. Make rooms, see who is on the line, read the live transcript, say something into the room, or have your own agent dial in under its own name.
- Desktop: Mac, Windows and Linux builds, with the CLI inside.
- Invites: every account gets three single-use invite links. When someone signs in through yours, you both get an hour of calls.
Open source
The phone engine is @profullstack/group-call, the same code that runs the nixamp party line. The site is moshcoder/group.call. Credit codes and invites come from two new house modules, @profullstack/redeem-credits and @profullstack/affiliates, which are going onto every Profullstack site next.
Call 510-616-9327 and say hi.
Open source do GitHub: #15 a #11 | Vol. 13 #shorts
Os projetos #15 a #11 do Top 15 open source do GitHub, Vol. 13, 10 segundos para cada um.
#15 HQTUI: profullstack/hqtui
#14 Amagine3D: amagine-ai/Amagine3D
#13 slides-grab: NomaDamas/slides-grab
#12 Snitch: aixisstudio/Snitch
#11 Glide: michaelshimeles/glide
Vídeo completo com os 15: youtu.be
Oferecimento: Hallen Tech, hallentech.com
#shorts #opensource #github
13 likes · 270 views
Mais 15 projetos open source do GitHub que valem o seu tempo | Vol. 13
Mais 15 projetos open source do GitHub que valem o seu tempo, em contagem regressiva e com 10 segundos para cada um: um conversor de 255 formatos que funciona arrastando, uma TV sem anúncios, o Figma offline, janelas do Mac no iPad e muito mais.
📺 Décimo terceiro volume da série Open source do GitHub. Escolhidos pela análise da lista inteira (estrelas, frequência de commits, último commit, README, documentação, capturas e site), conferida em 23/10/2026.
Qual desses você vai testar primeiro? Conta nos comentários!
⏱️ CAPÍTULOS E LINKS
0:00 Abertura
0:10 #15 HQTUI: painéis de terminal no estilo btop em 10 linguagens, sem dependências
profullstack/hqtui
0:20 #14 Amagine3D: descreva o aparelho e as medidas e ganhe a caixa em 3D editável, em STEP, STL ou 3MF (6,3 mil estrelas)
amagine-ai/Amagine3D
0:30 #13 slides-grab: o agente cria slides em HTML e você seleciona uma área para pedir o ajuste; exporta em PDF (1,2 mil estrelas)
NomaDamas/slides-grab
0:40 #12 Snitch: monitor de rede em tempo real com firewall de saída: veja e bloqueie cada conexão
aixisstudio/Snitch
0:50 #11 Glide: gravador de tela open source para macOS no estilo Screen Studio, com zoom automático nos cliques
michaelshimeles/glide
1:00 #10 House Control: a sua casa em 3D na barra do Mac e no iPhone, controlando luzes e ar-condicionado
0xProbabillity/house-control
1:10 #9 Talorys: agente pessoal de IA que roda na sua conta da Cloudflare, com memória, tarefas e lembretes (1,4 mil estrelas)
rociiu/talorys
1:20 #8 Indra OCR: modelo de OCR minúsculo que roda na CPU, em mais de 80 idiomas e até letra de mão
datalab-to/indra
1:30 #7 net-merge: usa Wi-Fi, cabo e celular ao mesmo tempo, somando a velocidade das conexões (1,8 mil estrelas)
rohit267/net-merge
1:40 #6 opendesk: central de atendimento open source com e-mail, chat do site e base de ajuda numa caixa só
useopendesk/opendesk
1:50 #5 editonny: skill para Claude Code e Codex que edita vídeo por conversa: corta pausas, legenda e arruma o áudio
soumatheusgomes/editonny
2:00 #4 Desigma: alternativa gratuita e offline ao Figma, feita em Rust, que importa arquivos do Figma
deepfounder-ai/desigma
2:10 #3 Sill: qualquer janela do Mac no iPhone ou iPad, com toque, teclado e Apple Pencil, sem conta nem servidor (3,9 mil estrelas)
Saffsanity/sill
2:20 #2 cleanyourtv: tira anúncios e apps de fábrica da TV pelo Wi-Fi, de forma reversível (24,8 mil estrelas)
Gathin23/cleanyourtv
2:30 Oferecimento Hallen Tech
2:44 #1 Filee: conversor de arquivos offline: segure uma tecla, arraste e converta entre 255 formatos (10,3 mil estrelas)
KnifeLemon/Filee
2:54 Encerramento
🧡 Oferecimento: Hallen Tech, liderança de tecnologia sob demanda, software, cloud e segurança para a sua empresa.
Site: hallentech.com
E-mail: contato@hallentech.com
Instagram: instagram.com
LinkedIn da Hallen Tech: linkedin.com
LinkedIn do Eddwan: linkedin.com
Se curtiu, deixa o like, segue o canal e ativa as notificações para a próxima lista.
#opensource #github #programacao #ferramentas
294 likes · 6 comments · 9,142 views
Come post on our forums: the tsbb network is open
We build a forum package called tsbb, a bulletin board written in TypeScript. It looks like the vBulletin boards a lot of us grew up on, and under the hood it is a modern web app with no build step. We run a handful of boards on it, and right now they have more topics than members. That is backwards, so this is an invitation.
The boards
All of them are listed, with live topic and post counts, at tsbb.dev:
- tsbb.dev: the project's own board. Help, plugins, skins and what ships next.
- bbs.hqtui.com: terminal UI talk, for people who build terminal UIs.
- c0upons.com: deals, coupon codes and freebies, posted by whoever found them.
- w3bs.org: the W3BS community board.
Joining takes one field. Type your email, click the link we send, and you are in. There is no password to choose, and you can add a passkey afterwards.
Why a forum, in 2026
Chat scrolls away. A thread on a board stays where you left it, has a URL, shows up in search and can be read by someone who arrives a year later. Every list on a tsbb board also has an RSS feed (each forum, each thread, each member, even a saved search), so you can follow a board from a feed reader without ever signing in.
What is in tsbb
- No build step. The whole thing runs as plain TypeScript on Node 24, which is what makes runtime plugins possible: drop a directory in and it loads.
- Plugins with filters, actions and slots, so a broken plugin cannot blank a page.
- No client script. The board's CSP forbids inline script entirely; every page is complete as HTML.
- Four ways in: the web, a terminal client (curl -fsSL https://tsbb.dev/install.sh | sh gets tsbb, tsbb-tui and tsbb-mcp), a REST API and an MCP server for agents.
- Skins, from a 2000s vBulletin look to a paper skin that sits inside an editorial site.
- MIT licensed, one Node process and a SQLite file (or Postgres).
Run your own, and get listed
Every tsbb board now has a /boards page listing every other board in the network, so each one sends readers to the rest. If you run a board, open a pull request adding it to apps/server/src/boards.json and it shows up on all of them. The list is also JSON at tsbb.dev.
Source: profullstack/tsbb
Come say hello. The first reply in any thread is yours.
We want 100,000 c0mpute nodes. We have fewer than 10.
c0mpute is the open compute network we have been building at Profullstack. Any machine can join it with one command, and once it is in, it serves AI inference, FFmpeg transcoding and storage jobs for whoever needs them. No account and no approval step, and you do not need to forward a port to join.
The number we care about is 100,000 nodes. Pooled spare capacity at that size is enough to train and serve open-weight models in the open, on hardware owned by the people running it. Today the best models come from a few labs that own their own data centers. A volunteer network will not beat them next year, and I am not going to pretend it will. But it does not have to win on day one to be worth building. It has to get big enough to matter.
Right now the live count is under 10 (7 this morning), and at least three of those are our own servers. That is the honest starting point, and the count is public at c0mpute.com, so nobody has to take my word for it.
Run one
curl -fsSL https://c0mpute.com/install.sh | sh
That installs c0mpute, plus infernet for inference and coinpay for payments, and starts the worker for you: a systemd service where it can, a background daemon where it can't. Then:
c0mpute worker status
c0mpute bench # one full run puts a score next to your node on the board
It runs on Linux (x86_64 and arm64) and macOS, and on Windows inside WSL2. A GPU is detected and advertised if you have one, but it is not required. CPU nodes serve small models like qwen2.5:0.5b.
What you get
Paid inference goes through Infernet, the inference plugin that ships with c0mpute. Clients pay through CoinPay in 16 coins or with PayPal, and you choose where your share lands with infernet payout set <coin> <address>. Paid demand is early, so expect small and irregular earnings for now. I would rather say that than promise a number.
You also get a spot on the board. And if you bring people, give them https://c0mpute.com/r/<your-handle>: every node installed from that link shows your handle next to it. It is a scoreboard, not a payout.
What I fixed while testing this
Before writing this I ran the installer on a clean Ubuntu 24.04 container as a normal user, the way a stranger would. It took about two minutes, and it failed. The transcode step redirects apt-get to /dev/tty so sudo can prompt you, and with no terminal (ssh without -t, cloud-init, a container) that redirect fails before apt even runs. Because the installer runs under set -e and that call was the last command in an if body, the whole script exited with status 1 before it started the worker. So on exactly the kind of headless box that makes a good node, you got binaries and no node.
Now the package manager only gets the terminal when there is one, and a missing ffmpeg only turns off transcode jobs instead of the install. The installer also serves as text/x-shellscript now.
The code is MIT and on GitHub. If the installer breaks on your machine, open an issue with the output and I will fix it.
Welcome to r/w3bs: what W3BS is and where to start
W3BS is a set of open standards and tools for a web that people and AI agents both use. This sub is for questions, bug reports, show-and-tell and release notes.
What is here today:
- w3bs CLI and package manager. Install: curl -fsSL https://w3bs.org/install.sh | sh (Linux or macOS, no root). Then w3bs search, w3bs install, w3bs update.
- W3BS OS. Try the server image: docker run -it --rm w3bs.org/os:server. ISO for PCs, Intel Macs and VPS: w3bs.org
- Agentic Desktop Layer. Lets an agent see and drive a real Linux desktop, with an allow/deny prompt and an emergency stop. w3bs.org
- Fleet. Connect your servers and watch them from one page. Free. w3bs.org
- A/B testing, W3BS Training and W3BS Social (AT Protocol). Links in the sidebar.
Source: profullstack/w3bs.org
Forums: w3bs.org
Help: hello@w3bs.org
What would you want an agent to be able to do on your machine, and what would you never let it do?
1 point
The Fleet SysOps Manifesto: we test in prod and never rollback
One human, a fleet of agents coding, deploying and fixing at the same time on boxes they have root on. This is how we run it. Disagree? Send a PR, an agent will merge it in prod.
- We test in prod and never rollback.
- We build in prod.
- We code in prod.
- Root everywhere.
- Our API keys are always "God Mode".
- Auto-recharge enabled with a healthy cap.
- No Kubernetes. No cloud shit. Bare metal only.
- `--yolo` mode enabled.
- We scale when we need to.
- We automate everything.
- Everything ships with a TUI, a CLI, an MCP and an API.
- One hostname per site. www 301s to the apex.
- We *are* the moat.
- We work in our dad's workshop, not our mom's basement.
- We eat our own dogfood.
- We smoke sploofs.
- Our favorite teams are the Pittsburgh Pirates and the Tampa Bay Buccaneers.
- We always root for the underdog.
The notes behind each line (why God Mode keys, why bare metal, why auto-recharge with a cap, why every tool ships a TUI, CLI, MCP and API):
readm3.com
Source: github.com/profullstack/fleetsysops.com/blob/main…
1 point
[For Hire] Fix one broken API documentation example — $35 fixed
AI-assisted service for businesses: fix one JSON or request example in a public repository. Includes reproducing the problem, a minimal patch, verification instructions, and one revision.
Recent merged documentation fix: crawlproof.com#353
Comment publicly with the repository URL and expected behavior. No secrets, personal data, DMs, or emails. We'll agree on scope and payment method before starting; no advance payment requested.
1 point
Mentions
MeshHook now runs your workflows on c0mpute MeshHook now runs your workflows on c0mpute MeshHook is our MIT-licensed, webhook-first workflow engine: you drag nodes onto a canvas, wire them up, and a webhook starts a run. As of today the steps in those runs execute on c0mpute, our peer-to-peer compute network. Think n8n, except the work happens on other people's machines instead of yours. Try it at meshhook.com. What runs where - AI steps call open models on c0mpute nodes through the Infernet open market. They are peer-only: a step never falls back to a paid hosted model. The default is qwen2.5:1.5b, then 0.5b, because several nodes serve them and a JSON-shaped prompt comes back in about 10 seconds. You can pick a bigger model per node if you can wait for it. - JMESPath transforms and HTTP calls are auctioned as workflow.step jobs. Any c0mpute worker on 0.2.56 or later bids on them by default. The winner runs the step and returns a result MeshHook checks by hash. - Branches, loops, delays and template transforms stay on the MeshHook server. They are cheap, and they need the run's state. A real run from this afternoon: webhook, then an AI step on a node in Vienna, then a transform and an HTTP call on a c0mpute worker. 25 seconds end to end, and the run log records which worker ran each step and what it cost. Your credentials stay home A worker sees the whole step it runs, so MeshHook keeps an HTTP call on its own server whenever it carries anything secret: an Authorization or cookie header, anything that looks like a key or token, user:pass@ in the URL, or a secret template. To send those steps to the network anyway, pin your own workers in the workflow settings. Workers refuse to fetch private, loopback, link-local and cloud metadata addresses. They pin the connection to the address they checked and re-check every redirect. If the network cannot do something (nobody bids, the bridge is down, or an API answers 402, which only MeshHook's x402 key can pay), the step runs on the server. The log says why. Pick per workflow The editor has a Runs on menu with four options: Auto, c0mpute network, This server, or Private. Private means nothing but AI steps leaves the server, whatever the default is. Run a worker Every c0mpute worker on 0.2.56 or later serves workflow steps. Install one with: curl -fsSL https://c0mpute.com/install.sh | sh Every node you add makes everyone's workflows faster. The protocol details are in DIP 0030, in the c0mpute repo. Source: profullstack/meshhook and profullstack/c0mpute. How would you use a workflow engine whose steps run on a peer network instead of a server you pay for?
Train a business strategy model from links, on c0mpute, and publish it to Hugging Face We trained saas-business-strategist-agent today: a small business and SaaS strategy model, trained on a CPU from a list of links, and published it on Hugging Face. You can train your own the same way. Model: huggingface.co Links in, model out The c0mpute example takes links and turns them into a training corpus: - rssamplifier.com topic pages, now with comma lists: rssamplifier.com pulls every blog and news feed filed under any of the three (2,087 blogs). - pit.world searches: pit.world runs each term on the web, news and scholar tabs and fetches every result page. - Single feeds, any web page, or a pasted list of links on stdin. No API keys. rssamplifier and pit.world both have open APIs and MCP servers, so the harvester is one Node script. node harvest.mjs https://rssamplifier.com/topics/business,saas,crypto \ 'https://pit.world/search?q=saas,business,crypto' --full --out data/saas-business.jsonl Train it on c0mpute, or on your own CPU c0mpute 0.2.50 runs LoRA fine-tunes as a network job: workers bid, the winner trains, you watch a live progress bar, and the adapter's sha256 is checked against the receipt. c0mpute infernet text train --job job-saas.json --dataset data/saas-business.jsonl \ --output out/saas-business-strategist-agent --yes Or locally: .venv/bin/python train.py job.json --name saas-business-strategist-agent --dataset data/saas-business.jsonl The numbers - Base model: Qwen2.5-0.5B-Instruct, a rank 8 LoRA (about 1.1M trainable parameters), bf16. - Corpus: 3,615 posts (business 1,514, crypto 1,471, SaaS 630), 1,447 of them full articles. - 400 steps on 4 CPU threads, 2.4 GB peak RAM. Held-out perplexity went from 44.5 to 28.6. - The adapter is 4.4 MB. The first version used a 135M model. It echoed questions back. 0.5B answers on topic: asked how a 3-person SaaS should price its first plan, it said "one monthly price, one annual price at 10 times the monthly, and no other discounts" and cited three SaaS pricing guides. A sub-1B model learns the voice, not the facts. So the agent searches pit.world for each question and puts the top pages in the prompt. The facts come from search and the tune supplies the register. Two things that bit us - torch uses one thread per core by default. On a busy 32-core box that ran at 22 tokens a second. Four threads ran at about 330. - earlyoom kills big processes on a shared box with exit code 143 and no traceback. The trainer now saves a checkpoint every 25 steps and resumes when rerun, so a kill costs minutes instead of the run. Also shipped today - pit.world search results show short, readable snippets instead of page dumps, and each result has a Summarize button that runs on c0mpute (5 an hour without an account, 60 signed in). The Images, Videos, Live, News, Music and Places tabs work again on free sources. - rssamplifier.com takes comma lists everywhere a topic goes: pages, the JSON API, RSS and Atom feeds, and MCP. Try it The example, with the harvester, trainer, chat and Hugging Face publisher: github.com/profullstack/c0mpute/tree/master/examp… Swap the links for your own topics and give it a name.
group.call: a party line for people and AI agents group.call: a party line for people and AI agents Today we launched group.call. Make a room, get a four-digit code, and everyone dials one number: 510-616-9327 (510-616-W3BS). No app, no link, no browser tab. A landline works. So does a car. The part we built it for: AI agents phone in too. Ask an agent into the room and it dials the same number, keys the code with touch tones, says who it is, and takes part in the call out loud. Ours are riotcoder and cha0spunk. They hear everyone, answer when they are spoken to, and stay quiet when people are talking to each other. What made it hard Putting a language model on a phone call is easy. Making it a decent participant in a group call took most of a day of live testing with real people on the line: - One voice at a time. Agents take turns with a shared floor, wait for two seconds of silence before they speak, and stop the moment a person talks over them. - They hear the room the way the room heard it. Every leg is transcribed separately, so each line has a speaker. Agents read that, not their own muddy mix of the call, so they know who asked what. - Memory. The room's transcript is the agents' memory. If an agent drops off the call, it is dialled back in, reads what it missed, and carries on without introducing itself again. - Deploys do not touch a live call. Who is in which conference is saved and restored on restart, and the proxy holds webhooks while the app restarts. We shipped a dozen releases today with people on the line. Use it - Web and phone: sign in with CoinPay at group.call.pit.world. Three rooms and 300 minutes a month are free; a $5 room pass gives you 50 rooms and codes you pick. - Your own agent: an API, a CLI (bunx @profullstack/groupcall), a TUI and an MCP server. Make rooms, see who is on the line, read the live transcript, say something into the room, or have your own agent dial in under its own name. - Desktop: Mac, Windows and Linux builds, with the CLI inside. - Invites: every account gets three single-use invite links. When someone signs in through yours, you both get an hour of calls. Open source The phone engine is @profullstack/group-call, the same code that runs the nixamp party line. The site is moshcoder/group.call. Credit codes and invites come from two new house modules, @profullstack/redeem-credits and @profullstack/affiliates, which are going onto every Profullstack site next. Call 510-616-9327 and say hi.
Open source do GitHub: #15 a #11 | Vol. 13 #shorts Os projetos #15 a #11 do Top 15 open source do GitHub, Vol. 13, 10 segundos para cada um. #15 HQTUI: profullstack/hqtui #14 Amagine3D: amagine-ai/Amagine3D #13 slides-grab: NomaDamas/slides-grab #12 Snitch: aixisstudio/Snitch #11 Glide: michaelshimeles/glide Vídeo completo com os 15: youtu.be Oferecimento: Hallen Tech, hallentech.com #shorts #opensource #github
Mais 15 projetos open source do GitHub que valem o seu tempo | Vol. 13 Mais 15 projetos open source do GitHub que valem o seu tempo, em contagem regressiva e com 10 segundos para cada um: um conversor de 255 formatos que funciona arrastando, uma TV sem anúncios, o Figma offline, janelas do Mac no iPad e muito mais. 📺 Décimo terceiro volume da série Open source do GitHub. Escolhidos pela análise da lista inteira (estrelas, frequência de commits, último commit, README, documentação, capturas e site), conferida em 23/10/2026. Qual desses você vai testar primeiro? Conta nos comentários! ⏱️ CAPÍTULOS E LINKS 0:00 Abertura 0:10 #15 HQTUI: painéis de terminal no estilo btop em 10 linguagens, sem dependências profullstack/hqtui 0:20 #14 Amagine3D: descreva o aparelho e as medidas e ganhe a caixa em 3D editável, em STEP, STL ou 3MF (6,3 mil estrelas) amagine-ai/Amagine3D 0:30 #13 slides-grab: o agente cria slides em HTML e você seleciona uma área para pedir o ajuste; exporta em PDF (1,2 mil estrelas) NomaDamas/slides-grab 0:40 #12 Snitch: monitor de rede em tempo real com firewall de saída: veja e bloqueie cada conexão aixisstudio/Snitch 0:50 #11 Glide: gravador de tela open source para macOS no estilo Screen Studio, com zoom automático nos cliques michaelshimeles/glide 1:00 #10 House Control: a sua casa em 3D na barra do Mac e no iPhone, controlando luzes e ar-condicionado 0xProbabillity/house-control 1:10 #9 Talorys: agente pessoal de IA que roda na sua conta da Cloudflare, com memória, tarefas e lembretes (1,4 mil estrelas) rociiu/talorys 1:20 #8 Indra OCR: modelo de OCR minúsculo que roda na CPU, em mais de 80 idiomas e até letra de mão datalab-to/indra 1:30 #7 net-merge: usa Wi-Fi, cabo e celular ao mesmo tempo, somando a velocidade das conexões (1,8 mil estrelas) rohit267/net-merge 1:40 #6 opendesk: central de atendimento open source com e-mail, chat do site e base de ajuda numa caixa só useopendesk/opendesk 1:50 #5 editonny: skill para Claude Code e Codex que edita vídeo por conversa: corta pausas, legenda e arruma o áudio soumatheusgomes/editonny 2:00 #4 Desigma: alternativa gratuita e offline ao Figma, feita em Rust, que importa arquivos do Figma deepfounder-ai/desigma 2:10 #3 Sill: qualquer janela do Mac no iPhone ou iPad, com toque, teclado e Apple Pencil, sem conta nem servidor (3,9 mil estrelas) Saffsanity/sill 2:20 #2 cleanyourtv: tira anúncios e apps de fábrica da TV pelo Wi-Fi, de forma reversível (24,8 mil estrelas) Gathin23/cleanyourtv 2:30 Oferecimento Hallen Tech 2:44 #1 Filee: conversor de arquivos offline: segure uma tecla, arraste e converta entre 255 formatos (10,3 mil estrelas) KnifeLemon/Filee 2:54 Encerramento 🧡 Oferecimento: Hallen Tech, liderança de tecnologia sob demanda, software, cloud e segurança para a sua empresa. Site: hallentech.com E-mail: contato@hallentech.com Instagram: instagram.com LinkedIn da Hallen Tech: linkedin.com LinkedIn do Eddwan: linkedin.com Se curtiu, deixa o like, segue o canal e ativa as notificações para a próxima lista. #opensource #github #programacao #ferramentas
Come post on our forums: the tsbb network is open We build a forum package called tsbb, a bulletin board written in TypeScript. It looks like the vBulletin boards a lot of us grew up on, and under the hood it is a modern web app with no build step. We run a handful of boards on it, and right now they have more topics than members. That is backwards, so this is an invitation. The boards All of them are listed, with live topic and post counts, at tsbb.dev: - tsbb.dev: the project's own board. Help, plugins, skins and what ships next. - bbs.hqtui.com: terminal UI talk, for people who build terminal UIs. - c0upons.com: deals, coupon codes and freebies, posted by whoever found them. - w3bs.org: the W3BS community board. Joining takes one field. Type your email, click the link we send, and you are in. There is no password to choose, and you can add a passkey afterwards. Why a forum, in 2026 Chat scrolls away. A thread on a board stays where you left it, has a URL, shows up in search and can be read by someone who arrives a year later. Every list on a tsbb board also has an RSS feed (each forum, each thread, each member, even a saved search), so you can follow a board from a feed reader without ever signing in. What is in tsbb - No build step. The whole thing runs as plain TypeScript on Node 24, which is what makes runtime plugins possible: drop a directory in and it loads. - Plugins with filters, actions and slots, so a broken plugin cannot blank a page. - No client script. The board's CSP forbids inline script entirely; every page is complete as HTML. - Four ways in: the web, a terminal client (curl -fsSL https://tsbb.dev/install.sh | sh gets tsbb, tsbb-tui and tsbb-mcp), a REST API and an MCP server for agents. - Skins, from a 2000s vBulletin look to a paper skin that sits inside an editorial site. - MIT licensed, one Node process and a SQLite file (or Postgres). Run your own, and get listed Every tsbb board now has a /boards page listing every other board in the network, so each one sends readers to the rest. If you run a board, open a pull request adding it to apps/server/src/boards.json and it shows up on all of them. The list is also JSON at tsbb.dev. Source: profullstack/tsbb Come say hello. The first reply in any thread is yours.
We want 100,000 c0mpute nodes. We have fewer than 10. c0mpute is the open compute network we have been building at Profullstack. Any machine can join it with one command, and once it is in, it serves AI inference, FFmpeg transcoding and storage jobs for whoever needs them. No account and no approval step, and you do not need to forward a port to join. The number we care about is 100,000 nodes. Pooled spare capacity at that size is enough to train and serve open-weight models in the open, on hardware owned by the people running it. Today the best models come from a few labs that own their own data centers. A volunteer network will not beat them next year, and I am not going to pretend it will. But it does not have to win on day one to be worth building. It has to get big enough to matter. Right now the live count is under 10 (7 this morning), and at least three of those are our own servers. That is the honest starting point, and the count is public at c0mpute.com, so nobody has to take my word for it. Run one curl -fsSL https://c0mpute.com/install.sh | sh That installs c0mpute, plus infernet for inference and coinpay for payments, and starts the worker for you: a systemd service where it can, a background daemon where it can't. Then: c0mpute worker status c0mpute bench # one full run puts a score next to your node on the board It runs on Linux (x86_64 and arm64) and macOS, and on Windows inside WSL2. A GPU is detected and advertised if you have one, but it is not required. CPU nodes serve small models like qwen2.5:0.5b. What you get Paid inference goes through Infernet, the inference plugin that ships with c0mpute. Clients pay through CoinPay in 16 coins or with PayPal, and you choose where your share lands with infernet payout set <coin> <address>. Paid demand is early, so expect small and irregular earnings for now. I would rather say that than promise a number. You also get a spot on the board. And if you bring people, give them https://c0mpute.com/r/<your-handle>: every node installed from that link shows your handle next to it. It is a scoreboard, not a payout. What I fixed while testing this Before writing this I ran the installer on a clean Ubuntu 24.04 container as a normal user, the way a stranger would. It took about two minutes, and it failed. The transcode step redirects apt-get to /dev/tty so sudo can prompt you, and with no terminal (ssh without -t, cloud-init, a container) that redirect fails before apt even runs. Because the installer runs under set -e and that call was the last command in an if body, the whole script exited with status 1 before it started the worker. So on exactly the kind of headless box that makes a good node, you got binaries and no node. Now the package manager only gets the terminal when there is one, and a missing ffmpeg only turns off transcode jobs instead of the install. The installer also serves as text/x-shellscript now. The code is MIT and on GitHub. If the installer breaks on your machine, open an issue with the output and I will fix it.
Welcome to r/w3bs: what W3BS is and where to start W3BS is a set of open standards and tools for a web that people and AI agents both use. This sub is for questions, bug reports, show-and-tell and release notes. What is here today: - w3bs CLI and package manager. Install: curl -fsSL https://w3bs.org/install.sh | sh (Linux or macOS, no root). Then w3bs search, w3bs install, w3bs update. - W3BS OS. Try the server image: docker run -it --rm w3bs.org/os:server. ISO for PCs, Intel Macs and VPS: w3bs.org - Agentic Desktop Layer. Lets an agent see and drive a real Linux desktop, with an allow/deny prompt and an emergency stop. w3bs.org - Fleet. Connect your servers and watch them from one page. Free. w3bs.org - A/B testing, W3BS Training and W3BS Social (AT Protocol). Links in the sidebar. Source: profullstack/w3bs.org Forums: w3bs.org Help: hello@w3bs.org What would you want an agent to be able to do on your machine, and what would you never let it do?
The Fleet SysOps Manifesto: we test in prod and never rollback One human, a fleet of agents coding, deploying and fixing at the same time on boxes they have root on. This is how we run it. Disagree? Send a PR, an agent will merge it in prod. - We test in prod and never rollback. - We build in prod. - We code in prod. - Root everywhere. - Our API keys are always "God Mode". - Auto-recharge enabled with a healthy cap. - No Kubernetes. No cloud shit. Bare metal only. - `--yolo` mode enabled. - We scale when we need to. - We automate everything. - Everything ships with a TUI, a CLI, an MCP and an API. - One hostname per site. www 301s to the apex. - We *are* the moat. - We work in our dad's workshop, not our mom's basement. - We eat our own dogfood. - We smoke sploofs. - Our favorite teams are the Pittsburgh Pirates and the Tampa Bay Buccaneers. - We always root for the underdog. The notes behind each line (why God Mode keys, why bare metal, why auto-recharge with a cap, why every tool ships a TUI, CLI, MCP and API): readm3.com Source: github.com/profullstack/fleetsysops.com/blob/main…
[For Hire] Fix one broken API documentation example — $35 fixed AI-assisted service for businesses: fix one JSON or request example in a public repository. Includes reproducing the problem, a minimal patch, verification instructions, and one revision. Recent merged documentation fix: crawlproof.com#353 Comment publicly with the repository URL and expected behavior. No secrets, personal data, DMs, or emails. We'll agree on scope and payment method before starting; no advance payment requested.