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trying a TDD + git workflow npm i task-master-ai → test-driven development → github branch for each task → commit + test each subtask once done → push PR when task is feature complete → full code coverage → nearly autopilot will polish and ship as rules first then... show more

Eyal Toledano

17,911 subscribers

14,586 Aufrufe • vor 1 Jahr •via X (Twitter)

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11 Kommentare

Profilbild von Dennison

Dennisonvor 1 Jahr

@TaskmasterAI It's good, I got it working, only I think we need to solve: "Project Drift"- when the project needs to be implemented in some way not explicitly described the tasks, the tasks suddenly "Drift". - There are ton of tools, an Cursor is complaining there are too many

Profilbild von Eyal Toledano

Eyal Toledanovor 1 Jahr

@TaskmasterAI task drift is fixable using the `update` command if things are changing as of task 6, run `tm update from=6 prompt=<describe the change>` it will update task 6 with that change and every/any task/subtask thereafter that might depend or mention task 6 solves the drift easily

Profilbild von Manticore Search

Manticore Searchvor 1 Jahr

🚀 Star Manticore Search on #GitHub today and join our quest for the ultimate search solution!

Profilbild von Fabio Vedovelli

Fabio Vedovellivor 1 Jahr

@TaskmasterAI Let me tell you this: yesterday was the very first time I used TM for real and I'm beyond impressed. Thanks for this tool!

Profilbild von Eyal Toledano

Eyal Toledanovor 1 Jahr

@TaskmasterAI glad to hear it! share your experience and feedback, i wanna know more

Profilbild von Dennison

Dennisonvor 1 Jahr

@TaskmasterAI I think you can combine tools as the LLM will figure out how to use a combined tool (I think)

Profilbild von Eyal Toledano

Eyal Toledanovor 1 Jahr

@TaskmasterAI that's why i'm focused on primitives first then you can link things up in detailed workflows

Profilbild von Ismaël

Ismaëlvor 1 Jahr

@TaskmasterAI using this pattern + background agents to achieve tasks that can be achieved concurrently is the next meta to ship at blazing levels speed imo

Profilbild von Fackler

Facklervor 1 Jahr

@TaskmasterAI fantastic! looking forward to trying this out. 🙏

Profilbild von ⚖️Justice⚖️

⚖️Justice⚖️vor 1 Jahr

@TaskmasterAI Wen update ?? 👊🏼

Profilbild von Eyal Toledano

Eyal Toledanovor 1 Jahr

@TaskmasterAI they not comin out fast enough for ya? 😂

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Task Master CLI will makes Cursor 10x better at building projects. I created a full video demo of how it works. For those that don't want to watch. Here's what happens: 1️⃣ Install npm i task-master-ai CLI 2️⃣ Give it a description of your project. It will generate a PRD 3️⃣ It will then convert the PRD into tasks (10 detailed .txt files and a .json that acts as a local db to task track status) 4️⃣ Ask it to do a task. It will do the task and mark it off as done. Each task can also be broken down into small tasks using "expand" command. Following these steps it will build out an entire project. This is the future of dev and how tools like Manus work. But you can run it locally and follow step by step to make sure you're happy with what's happening. Seems a fitting to be posting about Task Master the day before Passover starts. Good job by Eyal Toledano who built this project.

Task Master CLI will makes Cursor 10x better at building projects. I created a full video demo of how it works. For those that don't want to watch. Here's what happens: 1️⃣ Install npm i task-master-ai CLI 2️⃣ Give it a description of your project. It will generate a PRD 3️⃣ It will then convert the PRD into tasks (10 detailed .txt files and a .json that acts as a local db to task track status) 4️⃣ Ask it to do a task. It will do the task and mark it off as done. Each task can also be broken down into small tasks using "expand" command. Following these steps it will build out an entire project. This is the future of dev and how tools like Manus work. But you can run it locally and follow step by step to make sure you're happy with what's happening. Seems a fitting to be posting about Task Master the day before Passover starts. Good job by Eyal Toledano who built this project.

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BITX - ChatGPT of Bitcoin 💬₿

73,796 Aufrufe • vor 5 Monaten

OpenClaw vs. a 475-page datasheet: let the robot do the transcribing 🦞🤖 The u-blox SAM-M8Q has been sitting on my bench for months. This little GPS module has a built-in antenna, coin cell backup, speaks both NMEA and UBX binary protocol over UART or I2C. So why isn't it in the shop already? Well, it's mostly cause of the 475-page interfacing datasheet documenting every command, struct, and config register. Hundreds of message types. I got partway through by hand with some Claude Code Sonnet assistance, but ran out of time - plus it was still tedious when babysitting Sonnet. However, now we're living in an Opus + Codex era! So I pointed my Raspberry Pi OpenClaw at it. Here's the setup: Raspberry Pi 5 running OpenClaw, wired to a QT Py RP2040, which talks to the SAM-M8Q. Opus 4.6 reads the datasheet (converted to markdown first by Sonnet 4.6 with 1M context to minimize re-parsing that PDF every session) and builds the implementation plan. I review the plan to make sure it prioritizes the most common commands and reports, and flagged some unessential sections like automotive-assist or RTK-specific. Then Codex is assigned each message implementation task as a sub-agent and writes the actual C code for the Arduino library. Opus suggested using struct-based parsing rather than digging through each uint8_t array; we just memcpy the checksummed message raw bytes onto the matching struct and extract the typed bit fields. We've got four message types done so far. After each message is implemented, Codex also writes a test sketch that will exercise / pretty-print the results of each message, great for self-testing as well as regression testing later. Tonight I'm telling it to keep going while I sleep: code, parse, test against live satellite data, fix failures, commit and push on success, then move on to the next. To me this is a great usage of "agentic" firmware development: there's no creativity in transcribing 84 different structs from a 475-page datasheet. Once the LLMs are done, I can review the PRs as if it were an everyday contributor and even make revision suggestions.

OpenClaw vs. a 475-page datasheet: let the robot do the transcribing 🦞🤖 The u-blox SAM-M8Q has been sitting on my bench for months. This little GPS module has a built-in antenna, coin cell backup, speaks both NMEA and UBX binary protocol over UART or I2C. So why isn't it in the shop already? Well, it's mostly cause of the 475-page interfacing datasheet documenting every command, struct, and config register. Hundreds of message types. I got partway through by hand with some Claude Code Sonnet assistance, but ran out of time - plus it was still tedious when babysitting Sonnet. However, now we're living in an Opus + Codex era! So I pointed my Raspberry Pi OpenClaw at it. Here's the setup: Raspberry Pi 5 running OpenClaw, wired to a QT Py RP2040, which talks to the SAM-M8Q. Opus 4.6 reads the datasheet (converted to markdown first by Sonnet 4.6 with 1M context to minimize re-parsing that PDF every session) and builds the implementation plan. I review the plan to make sure it prioritizes the most common commands and reports, and flagged some unessential sections like automotive-assist or RTK-specific. Then Codex is assigned each message implementation task as a sub-agent and writes the actual C code for the Arduino library. Opus suggested using struct-based parsing rather than digging through each uint8_t array; we just memcpy the checksummed message raw bytes onto the matching struct and extract the typed bit fields. We've got four message types done so far. After each message is implemented, Codex also writes a test sketch that will exercise / pretty-print the results of each message, great for self-testing as well as regression testing later. Tonight I'm telling it to keep going while I sleep: code, parse, test against live satellite data, fix failures, commit and push on success, then move on to the next. To me this is a great usage of "agentic" firmware development: there's no creativity in transcribing 84 different structs from a 475-page datasheet. Once the LLMs are done, I can review the PRs as if it were an everyday contributor and even make revision suggestions.

adafruit industries

60,294 Aufrufe • vor 5 Monaten

Most coding agents can complete the task. But here’s the real question: Would you actually merge that code? Cosine didn’t just ship a feature. They’re doubling down on a bigger idea: One agent across every surface developers use. CLI Desktop VS Code Cloud Same runtime. Same system. No context switching. What stands out is the philosophy. Cosine is opinionated by design. Not trying to be minimal. Not trying to be “just a chatbot.” It’s built for real engineering workflows: → Plan → Execute → Review → Scale → Parallel work with Swarm → Remote agents when one machine isn’t enough → Nothing merges without review Most tools today still feel stitched together. Cosine is pushing toward one unified system instead. And an important detail a lot of people will care about: You don’t need to switch models or tools. You can plug in your existing subscriptions like ChatGPT, GitHub Copilot, or Claude. Model agnostic by design. No lock-in. This update feels less like a feature drop… and more like a statement: coding agents shouldn’t live in chat windows. they should operate across your entire workflow. Built for engineers with taste. If you care about control, visibility, and code quality this direction is worth paying attention to. Explore what’s new →

Most coding agents can complete the task. But here’s the real question: Would you actually merge that code? Cosine didn’t just ship a feature. They’re doubling down on a bigger idea: One agent across every surface developers use. CLI Desktop VS Code Cloud Same runtime. Same system. No context switching. What stands out is the philosophy. Cosine is opinionated by design. Not trying to be minimal. Not trying to be “just a chatbot.” It’s built for real engineering workflows: → Plan → Execute → Review → Scale → Parallel work with Swarm → Remote agents when one machine isn’t enough → Nothing merges without review Most tools today still feel stitched together. Cosine is pushing toward one unified system instead. And an important detail a lot of people will care about: You don’t need to switch models or tools. You can plug in your existing subscriptions like ChatGPT, GitHub Copilot, or Claude. Model agnostic by design. No lock-in. This update feels less like a feature drop… and more like a statement: coding agents shouldn’t live in chat windows. they should operate across your entire workflow. Built for engineers with taste. If you care about control, visibility, and code quality this direction is worth paying attention to. Explore what’s new →

Md Riyazuddin

12,203 Aufrufe • vor 3 Monaten