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just shipped taskmaster v0.15 🚀 → add-task is now context-aware 🔥 → research flag for parse-prd → new move command → analyze-complexity of specific tasks → add tasks without a PRD → ollama improved → celebrating 10k ⭐️ on GitHub +++ follow + bookmark + dive in 👀👇

Eyal Toledano

17,869 subscribers

15,474 次观看 • 1 年前 •via X (Twitter)

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just shipped taskmaster v0.14 🚀 → know the cost of your taskmaster calls → ollama provider support → baseUrl support across roles → task complexity score in tasks → strong focus on fixes & stability → over 9,500⭐ on GitHub follow + bookmark + dive in 👀👇

just shipped taskmaster v0.14 🚀 → know the cost of your taskmaster calls → ollama provider support → baseUrl support across roles → task complexity score in tasks → strong focus on fixes & stability → over 9,500⭐ on GitHub follow + bookmark + dive in 👀👇

Eyal Toledano

14,360 次观看 • 1 年前

just shipped taskmaster v0.16 🚀 → .taskmaster folder → bedrock, @azure & Google AI vertex → TASK_MASTER_PROJECT_ROOT & BaseURL → semantic context for add-task → README <> task list sync + welcoming legend Jason Zhou to @taskmasterai follow + bookmark + dive in 👀👇

just shipped taskmaster v0.16 🚀 → .taskmaster folder → bedrock, @azure & Google AI vertex → TASK_MASTER_PROJECT_ROOT & BaseURL → semantic context for add-task → README <> task list sync + welcoming legend Jason Zhou to @taskmasterai follow + bookmark + dive in 👀👇

Eyal Toledano

69,520 次观看 • 1 年前

we've shipped taskmaster v0.27 🚀 → introducing `start` command → taskmaster auto-installs updates → grok CLI support GrokCli → rules & config Kilo → smart parent task status → move tasks across tags → gpt-oss support + loads more follow + retweet + bookmark 👀👇

we've shipped taskmaster v0.27 🚀 → introducing `start` command → taskmaster auto-installs updates → grok CLI support GrokCli → rules & config Kilo → smart parent task status → move tasks across tags → gpt-oss support + loads more follow + retweet + bookmark 👀👇

Eyal Toledano

11,376 次观看 • 1 年前

just shipped taskmaster v0.17 🚀 → multi-context task management via tagged lists → `research` MCP tool with interactive CLI → get-task/show upgrades & batch ops → --append for update-task → save research to docs → multi-status filters follow + bookmark + dive in 👀👇

just shipped taskmaster v0.17 🚀 → multi-context task management via tagged lists → `research` MCP tool with interactive CLI → get-task/show upgrades & batch ops → --append for update-task → save research to docs → multi-status filters follow + bookmark + dive in 👀👇

Eyal Toledano

37,012 次观看 • 1 年前

just shipped taskmaster v0.18 🚀 → Claude Code Community provider without API keys → init with IDE-specific profiles for Cursor Claude Code Community @windsurf Cline Visual Studio Code TRAE → cleaner git repos & Python support → PROVIDER_BASE_URL follow + bookmark + dive in 👀👇

just shipped taskmaster v0.18 🚀 → Claude Code Community provider without API keys → init with IDE-specific profiles for Cursor Claude Code Community @windsurf Cline Visual Studio Code TRAE → cleaner git repos & Python support → PROVIDER_BASE_URL follow + bookmark + dive in 👀👇

Eyal Toledano

85,994 次观看 • 1 年前

I use subagents for ~80% of my Codex tasks these days. If you're new to subagents, here's a quick primer: - Subagents = separate agents that work on a specific piece of work in a fresh isolated context window. - Your main thread can create subagents and delegate work to them. - Subagents report back when tasks are complete. - Codex subagents are especially useful for read-heavy tasks (e.g research, investigation, and review). - Also useful for testing and building parts of a project in parallel. Subagents are great for working on focused parts of a big task, like planning, implementation, testing, or review. My biggest pro tip for using subagents in Codex: You don’t have to design the entire delegation plan yourself. Just ask Codex to “Use subagents as needed,” and then Codex can then decide where subagents would help it in the task. Here's a demo of using subagents in Codex:

I use subagents for ~80% of my Codex tasks these days. If you're new to subagents, here's a quick primer: - Subagents = separate agents that work on a specific piece of work in a fresh isolated context window. - Your main thread can create subagents and delegate work to them. - Subagents report back when tasks are complete. - Codex subagents are especially useful for read-heavy tasks (e.g research, investigation, and review). - Also useful for testing and building parts of a project in parallel. Subagents are great for working on focused parts of a big task, like planning, implementation, testing, or review. My biggest pro tip for using subagents in Codex: You don’t have to design the entire delegation plan yourself. Just ask Codex to “Use subagents as needed,” and then Codex can then decide where subagents would help it in the task. Here's a demo of using subagents in Codex:

Avthar

18,444 次观看 • 1 个月前

I’ve completed all the tasks on Axis Robotics I Made a Video Showing How to Complete Axis Robotics Tasks Easily! Many people are getting stuck while completing Axis Robotics tasks, and some of you asked me to make a detailed video So I made a step-by-step video showing the easiest way to complete the tasks If you haven’t started yet, you can still join early. The project has already raised around $12M, and the Points System is now LIVE 🔗 > Create / log in to your account > Go to Training Tasks > Complete the available tasks > Submit your work properly > Sign your completed tasks > Keep completing different tasks to build your Points Points depend on more than just activity: - Task difficulty - Number of valid tasks - Quality of your submissions - Diversity of tasks - Genuine referrals Don’t keep repeating the same task, Focus on completing different and higher-quality tasks If you’re new to Axis, I recommend starting early and learning the process now instead of waiting until more users join I explained the full process in my video so you can follow it easily Who’s already farming Axis Points?

I’ve completed all the tasks on Axis Robotics I Made a Video Showing How to Complete Axis Robotics Tasks Easily! Many people are getting stuck while completing Axis Robotics tasks, and some of you asked me to make a detailed video So I made a step-by-step video showing the easiest way to complete the tasks If you haven’t started yet, you can still join early. The project has already raised around $12M, and the Points System is now LIVE 🔗 > Create / log in to your account > Go to Training Tasks > Complete the available tasks > Submit your work properly > Sign your completed tasks > Keep completing different tasks to build your Points Points depend on more than just activity: - Task difficulty - Number of valid tasks - Quality of your submissions - Diversity of tasks - Genuine referrals Don’t keep repeating the same task, Focus on completing different and higher-quality tasks If you’re new to Axis, I recommend starting early and learning the process now instead of waiting until more users join I explained the full process in my video so you can follow it easily Who’s already farming Axis Points?

Shadat Official | Airdrop

23,952 次观看 • 2 个月前

Are you ready? AGIX Ecosystem has Arrived!🚀 [1/10] AGIX is the world’s first AGI & AI Agents marketplace in Web3. Unlike ChatGPT, which handles single tasks, AGIX powers multi-agent systems to tackle complex tasks — all orchestrated by our AGI core. 🔍 Watch Our CTO Vlad Demonstrate the Power of AGIX: • Text-to-Action — See AGIX not just analyze, but act in real-time. • Multi-Tool Integration — Hundreds of tools at its command for any task. • Multi-Cycle Task Processing — Self-refining AI that iterates for perfection. • Full Autonomy — Give AGIX a complex task and let it handle the rest, with live updates in Telegram. It's the beginning of an AI Agents Economy.

Are you ready? AGIX Ecosystem has Arrived!🚀 [1/10] AGIX is the world’s first AGI & AI Agents marketplace in Web3. Unlike ChatGPT, which handles single tasks, AGIX powers multi-agent systems to tackle complex tasks — all orchestrated by our AGI core. 🔍 Watch Our CTO Vlad Demonstrate the Power of AGIX: • Text-to-Action — See AGIX not just analyze, but act in real-time. • Multi-Tool Integration — Hundreds of tools at its command for any task. • Multi-Cycle Task Processing — Self-refining AI that iterates for perfection. • Full Autonomy — Give AGIX a complex task and let it handle the rest, with live updates in Telegram. It's the beginning of an AI Agents Economy.

AGIX | $AGX

95,359 次观看 • 1 年前

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,200 次观看 • 1 年前

🌌 GPT-6 Astra from OpenAI Developers is now generally available in GitHub Copilot. This new model is designed for long-horizon, autonomous coding and agentic tasks. Our internal testing shows it plans and validates as it goes, batches diagnosis with verification, and independently confirms its results before declaring a task done. That translated into stronger performance on extensive coding jobs with fewer steps than prior OpenAI models. Try it out in the GitHub Copilot app, CLI, or Visual Studio Code. 🚀

🌌 GPT-6 Astra from OpenAI Developers is now generally available in GitHub Copilot. This new model is designed for long-horizon, autonomous coding and agentic tasks. Our internal testing shows it plans and validates as it goes, batches diagnosis with verification, and independently confirms its results before declaring a task done. That translated into stronger performance on extensive coding jobs with fewer steps than prior OpenAI models. Try it out in the GitHub Copilot app, CLI, or Visual Studio Code. 🚀

GitHub

175,100 次观看 • 1 个月前

Together AI just raised $800M at an $8.3B valuation. I got their product team to open up the actual repo they run on: 0:00 - Slop is the new party foul 1:44 - Why individual output backfired 3:26 - Inside the shared product repo 8:57 - Who owns the context files 10:41 - Team skills vs personal skills 11:57 - Picking a harness and a model 15:17 - Feature research running live 16:57 - What stays human in the loop 19:56 - The PRD skill that interviews you 23:09 - Half a day of research in 5 min 24:25 - Their customer insights MCP 27:31 - What a good PRD looks like now 32:02 - Reading the finished PRD 35:07 - Every repo in one place 40:24 - Context is a hierarchy, not a pool 41:20 - Build your own orchestrator 44:19 - Agent evals on shipped features 49:41 - Design validated every few hours 52:26 - Where PM ends, engineer begins 55:10 - What it actually cost them

Together AI just raised $800M at an $8.3B valuation. I got their product team to open up the actual repo they run on: 0:00 - Slop is the new party foul 1:44 - Why individual output backfired 3:26 - Inside the shared product repo 8:57 - Who owns the context files 10:41 - Team skills vs personal skills 11:57 - Picking a harness and a model 15:17 - Feature research running live 16:57 - What stays human in the loop 19:56 - The PRD skill that interviews you 23:09 - Half a day of research in 5 min 24:25 - Their customer insights MCP 27:31 - What a good PRD looks like now 32:02 - Reading the finished PRD 35:07 - Every repo in one place 40:24 - Context is a hierarchy, not a pool 41:20 - Build your own orchestrator 44:19 - Agent evals on shipped features 49:41 - Design validated every few hours 52:26 - Where PM ends, engineer begins 55:10 - What it actually cost them

Aakash Gupta

78,445 次观看 • 23 天前

Hermes Agent’s Kanban just got a MASSIVE UI upgrade. 🪽 And this is way more than a fresh coat of paint. - New two-column task view - Comments, Activity, Runs + Worker Logs in one clean feed - Dependencies show actual linked tasks - Full Markdown rendering - Cleaner properties, spacing + controls It’s starting to feel less like an “AI feature” and more like an actual command center for autonomous work. Nous Research absolutely cooked on this one. Watch this. 👇

Hermes Agent’s Kanban just got a MASSIVE UI upgrade. 🪽 And this is way more than a fresh coat of paint. - New two-column task view - Comments, Activity, Runs + Worker Logs in one clean feed - Dependencies show actual linked tasks - Full Markdown rendering - Cleaner properties, spacing + controls It’s starting to feel less like an “AI feature” and more like an actual command center for autonomous work. Nous Research absolutely cooked on this one. Watch this. 👇

Tony Simons

55,389 次观看 • 12 天前

🚀 Introducing Queue: A Smarter Way to Build with Replit Agent Today, we’re excited to launch Queue, a powerful new capability designed to enhance your experience with Replit Agent. With Queue, you can submit multiple tasks while your Agent is still working—no more waiting around or breaking your flow. As each task completes, the Agent automatically moves to the next. You can: ✅ Add multiple tasks for the Agent to process in order. ✅ Edit, reorder, or reprioritize messages on the fly. ✅ Attach files and adjust settings—including toggling extended thinking or selecting a different model—per task.

🚀 Introducing Queue: A Smarter Way to Build with Replit Agent Today, we’re excited to launch Queue, a powerful new capability designed to enhance your experience with Replit Agent. With Queue, you can submit multiple tasks while your Agent is still working—no more waiting around or breaking your flow. As each task completes, the Agent automatically moves to the next. You can: ✅ Add multiple tasks for the Agent to process in order. ✅ Edit, reorder, or reprioritize messages on the fly. ✅ Attach files and adjust settings—including toggling extended thinking or selecting a different model—per task.

Replit ⠕

75,743 次观看 • 1 年前

update: i gave chatgpt full access to the taskmaster github repo and asked it to produce a thorough investigation of the product, the code and architecture, and to pay special attention to LLM-powered test generation, which I particularly care about right now it responded with an 8-page Mackenzie consultant-level report that dives deep into everything I asked and more one really solid thing it did that taskmaster makes possible is I asked it to go through all of my Taskmaster task files and to give recommendations on which are most high value/high priority. While I already had researched these tasks, it does add value to get another perspective i’ve been used to this with o1 pro, but to see the level of reasoning achieved against a growing and reasonably large code base blew my mind overall, it provided: - general audit and best practice recommendations - suggestions for new features - recommendations for how to consolidate MCP tools from 25 to 5-6 - review of open npm i task-master-ai tasks - exploration of LLM powered test generation with full references of where it grabbed that information absolutely incredible

update: i gave chatgpt full access to the taskmaster github repo and asked it to produce a thorough investigation of the product, the code and architecture, and to pay special attention to LLM-powered test generation, which I particularly care about right now it responded with an 8-page Mackenzie consultant-level report that dives deep into everything I asked and more one really solid thing it did that taskmaster makes possible is I asked it to go through all of my Taskmaster task files and to give recommendations on which are most high value/high priority. While I already had researched these tasks, it does add value to get another perspective i’ve been used to this with o1 pro, but to see the level of reasoning achieved against a growing and reasonably large code base blew my mind overall, it provided: - general audit and best practice recommendations - suggestions for new features - recommendations for how to consolidate MCP tools from 25 to 5-6 - review of open npm i task-master-ai tasks - exploration of LLM powered test generation with full references of where it grabbed that information absolutely incredible

Eyal Toledano

12,684 次观看 • 1 年前

Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.

Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.

AK

305,683 次观看 • 3 年前

The robot is learning several novel tasks instantly, after just ONE demonstration each... Instant Policy makes it possible: no extra training, no weight updates, just pure in-context learning. It just got accepted at ICLR 2025, and it’s changing how robots learn. With just a single demo, a robot can pick up a new task and start performing it right away. Why this is a big deal: ✅ Learns tasks instantly with just one or a few demonstrations ✅ Improves over time as more demonstrations are given ✅ Uses simulation-based training with “pseudo-demonstrations” for scalability ✅ Can transfer skills across different robots and even follow language-defined tasks It brings in-context learning to robotics, opening up new possibilities for flexible, real-world automation. You can try it yourself: code and weights are available at • • • • Thank you to Edward Johns, Director of the Robot Learning Lab at Imperial College for sharing their work! 🙏

The robot is learning several novel tasks instantly, after just ONE demonstration each... Instant Policy makes it possible: no extra training, no weight updates, just pure in-context learning. It just got accepted at ICLR 2025, and it’s changing how robots learn. With just a single demo, a robot can pick up a new task and start performing it right away. Why this is a big deal: ✅ Learns tasks instantly with just one or a few demonstrations ✅ Improves over time as more demonstrations are given ✅ Uses simulation-based training with “pseudo-demonstrations” for scalability ✅ Can transfer skills across different robots and even follow language-defined tasks It brings in-context learning to robotics, opening up new possibilities for flexible, real-world automation. You can try it yourself: code and weights are available at • • • • Thank you to Edward Johns, Director of the Robot Learning Lab at Imperial College for sharing their work! 🙏

Ilir Aliu

46,285 次观看 • 1 年前

We're killing "remind me to do it" today. A reminder won't do your work, it'll just tell you the work is still yours. Enter Scheduled Tasks on Runable. From something simple like "send me a summary of my day" To comically complex like "every Monday, research my competitors, analyze their ads, find gaps in our positioning, and have a GTM brief sent to my team on Slack before I’ve had my coffee." Whatever the task. Tell Runable to schedule it and it'll get done, without fail, every time.

We're killing "remind me to do it" today. A reminder won't do your work, it'll just tell you the work is still yours. Enter Scheduled Tasks on Runable. From something simple like "send me a summary of my day" To comically complex like "every Monday, research my competitors, analyze their ads, find gaps in our positioning, and have a GTM brief sent to my team on Slack before I’ve had my coffee." Whatever the task. Tell Runable to schedule it and it'll get done, without fail, every time.

Umesh Kumar

268,555 次观看 • 14 天前

🚀 We just shipped a big upgrade to our imitation-learning-in-simulation playground in LeRobot, built together with Lightwheel ! You can now teleoperate robots in sim (keyboard or real robot) and collect training demos instantly. This makes it possible to run real IL research on harder, more realistic manipulation tasks, even if you don’t have hardware. New tasks: 🟠 pick orange to the plate 🧺 fold cloth (yes!) 📦 pick 2 “e” toys to the box 🔴 lift red cube Through our partnership with Lightwheel, LeIsaac was integrated into EnvHub on day one, a best-practice integration that strengthens both ecosystems and pushes simulation-first robotics forward. 👉 Load a task via EnvHub, start teleop-ing, record your demo, upload the data on the hub and you’re ready to train or test.

🚀 We just shipped a big upgrade to our imitation-learning-in-simulation playground in LeRobot, built together with Lightwheel ! You can now teleoperate robots in sim (keyboard or real robot) and collect training demos instantly. This makes it possible to run real IL research on harder, more realistic manipulation tasks, even if you don’t have hardware. New tasks: 🟠 pick orange to the plate 🧺 fold cloth (yes!) 📦 pick 2 “e” toys to the box 🔴 lift red cube Through our partnership with Lightwheel, LeIsaac was integrated into EnvHub on day one, a best-practice integration that strengthens both ecosystems and pushes simulation-first robotics forward. 👉 Load a task via EnvHub, start teleop-ing, record your demo, upload the data on the hub and you’re ready to train or test.

LeRobot

44,892 次观看 • 10 个月前

540,000 lines of Rails is a brutal way to learn the agent era Garry's List shipped with: > 262,000 lines of app code > 276,000 lines of tests > 127 background jobs > 33 cron jobs > a 1,778-line claim verifier Then GStack hit 105k GitHub stars Do not copy the Rails pattern 1. Finish the task with an agent 2. Say `skillify it` 3. Save the markdown skill 4. Keep TypeScript only for deterministic I/O 5. Add a unit test for the code 6. Add an LLM eval for the skill 7. Add an integration test across both 8. Add a resolver so the agent loads the skill next time A Rails feature serves one app A skill pack can run against the next hackathon, repo, customer inbox, research folder, or QA run Audit your repo this week Cut code that only exists to: > re-check model output > retry the same model call > validate what the model already has context for > schedule work because the agent has no reusable procedure > preserve a one-off prompt in chat history Keep the code that touches files, APIs, auth, billing, devices, databases Move the judgment into markdown Test the markdown Reuse the skill

540,000 lines of Rails is a brutal way to learn the agent era Garry's List shipped with: > 262,000 lines of app code > 276,000 lines of tests > 127 background jobs > 33 cron jobs > a 1,778-line claim verifier Then GStack hit 105k GitHub stars Do not copy the Rails pattern 1. Finish the task with an agent 2. Say `skillify it` 3. Save the markdown skill 4. Keep TypeScript only for deterministic I/O 5. Add a unit test for the code 6. Add an LLM eval for the skill 7. Add an integration test across both 8. Add a resolver so the agent loads the skill next time A Rails feature serves one app A skill pack can run against the next hackathon, repo, customer inbox, research folder, or QA run Audit your repo this week Cut code that only exists to: > re-check model output > retry the same model call > validate what the model already has context for > schedule work because the agent has no reusable procedure > preserve a one-off prompt in chat history Keep the code that touches files, APIs, auth, billing, devices, databases Move the judgment into markdown Test the markdown Reuse the skill

Alpha Batcher

36,878 次观看 • 4 个月前

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇

YanXbt

187,943 次观看 • 4 个月前