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If you ship with a coding agent and still click through every PR by hand, this 40 second clip is worth watching. TestSprite picks up the pull request, runs Integration Tests against the running app in a real browser, and reports passed, failed, and blocked separately, so a broken...

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Elise Tech Ai

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36,477 görüntüleme • 23 gün önce •via X (Twitter)

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Most teams' definition of done was written for people. "Tests pass" was shorthand, and everyone on the team knew it also meant you clicked through the thing once. Agents read it literally. If your AGENTS.md says done means tests pass, the cheapest path to done is making tests pass, and editing a test is often cheaper than fixing the code. Write a definition the agent cannot satisfy by editing a file: done means the user flow works in the running app. Then give it a way to check that on its own. The TestSprite CLI is open source under Apache-2.0, and an agent can call it directly to run Integration Tests against the app before it reports back.
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Most teams' definition of done was written for people. "Tests pass" was shorthand, and everyone on the team knew it also meant you clicked through the thing once. Agents read it literally. If your AGENTS.md says done means tests pass, the cheapest path to done is making tests pass, and editing a test is often cheaper than fixing the code. Write a definition the agent cannot satisfy by editing a file: done means the user flow works in the running app. Then give it a way to check that on its own. The TestSprite CLI is open source under Apache-2.0, and an agent can call it directly to run Integration Tests against the app before it reports back.

Alice The Ai Expert

24,005 görüntüleme • 23 gün önce

this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.
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this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.

NO1ennn

21,527 görüntüleme • 1 ay önce

An hour of live AI video generation now costs $10.80 At $0.003 a second, Vivix W1 generates a full minute for just 18 cents. That price matters because of what W1 is. It's a real-time interactive video model. Through the API, it keeps generating video while the experience is happening, and you can shape what comes next. With a normal clip generator, you pay once and you're done. Real-time interactive video keeps generating as you use it. Every second generated is another second you pay for. At higher per-second rates, that bill can grow quickly. It's hard to ship an interactive product when every user session costs a fortune. With W1, the math starts to look workable. And they didn't buy that price by cutting quality or speed. In Vivix’s supplied tests, eight-second videos averaged 7.7 seconds to generate from text and 8.4 seconds with one reference image. In the side-by-side comparisons, W1 holds its own against Seedance 2.5, Kling 3.0, and H3 Max on the same prompts. An exploding taxi, a skyline shot, a robot serving drinks on a plane. It's not perfect yet: • Cinematic fidelity still has gaps • Complex motion and dense scenes still need work • Full-scale training is still underway, so this is an early checkpoint How to try it: • Real-time generation is live through the API today • The browser playground does offline generation for now. Submit a prompt and you get a finished clip. Real-time browser tools are coming soon My take: I've seen a lot of interactive video demos that would cost a fortune to run in production. This looks priced for something you could actually ship. Once a minute of live video generation costs 18 cents, interactive stories, games, and characters could become an everyday medium instead of a tech demo. What I want to see next from Vivix.AI: better complex motion. Improve that and the use cases open up fast. Put your project’s runtime into the equation 👇
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An hour of live AI video generation now costs $10.80 At $0.003 a second, Vivix W1 generates a full minute for just 18 cents. That price matters because of what W1 is. It's a real-time interactive video model. Through the API, it keeps generating video while the experience is happening, and you can shape what comes next. With a normal clip generator, you pay once and you're done. Real-time interactive video keeps generating as you use it. Every second generated is another second you pay for. At higher per-second rates, that bill can grow quickly. It's hard to ship an interactive product when every user session costs a fortune. With W1, the math starts to look workable. And they didn't buy that price by cutting quality or speed. In Vivix’s supplied tests, eight-second videos averaged 7.7 seconds to generate from text and 8.4 seconds with one reference image. In the side-by-side comparisons, W1 holds its own against Seedance 2.5, Kling 3.0, and H3 Max on the same prompts. An exploding taxi, a skyline shot, a robot serving drinks on a plane. It's not perfect yet: • Cinematic fidelity still has gaps • Complex motion and dense scenes still need work • Full-scale training is still underway, so this is an early checkpoint How to try it: • Real-time generation is live through the API today • The browser playground does offline generation for now. Submit a prompt and you get a finished clip. Real-time browser tools are coming soon My take: I've seen a lot of interactive video demos that would cost a fortune to run in production. This looks priced for something you could actually ship. Once a minute of live video generation costs 18 cents, interactive stories, games, and characters could become an everyday medium instead of a tech demo. What I want to see next from Vivix.AI: better complex motion. Improve that and the use cases open up fast. Put your project’s runtime into the equation 👇

Raja Chichulkar

19,396 görüntüleme • 27 gün önce

Introducing Expect Let agents test your code in a real browser 1. Run Claude Code / Codex to QA your app 2. Watch a video of every bug found 3. Fix and repeat until passing Run as a CLI or agent skill. Fully open source
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Introducing Expect Let agents test your code in a real browser 1. Run Claude Code / Codex to QA your app 2. Watch a video of every bug found 3. Fix and repeat until passing Run as a CLI or agent skill. Fully open source

Aiden Bai

847,527 görüntüleme • 7 ay önce

Vorflux has opened its new cloud platform, allowing users to run tasks from a plan to a merged PR on a dedicated machine. It can plan, build, test, and review on its own. > Diff reviews are done by a different model family than the one that wrote it by default. > First boot is saved as a snapshot, with all subsequent sessions waking up from that same starting point. > A browser agent walks through the real user flow, and the recording is included in the pull request. Video proof in the PR 👀
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Vorflux has opened its new cloud platform, allowing users to run tasks from a plan to a merged PR on a dedicated machine. It can plan, build, test, and review on its own. > Diff reviews are done by a different model family than the one that wrote it by default. > First boot is saved as a snapshot, with all subsequent sessions waking up from that same starting point. > A browser agent walks through the real user flow, and the recording is included in the pull request. Video proof in the PR 👀

🚨 AI News | TestingCatalog

12,327 görüntüleme • 2 ay önce

THE DESK RAN SIX DAYS FOR FREE AND CLOSED 96% OF 400 CALLS BEFORE IT HAD A TICKER -> TODAY IT GETS ONE $NYSE IS LIVE ON ROBINHOOD CHAIN, BUILT WITH MINARA AI AND GPT-6 ASTRA, AND THE SCORECARD IS OLDER THAN THE CHART CA: 0x3cedefe814e47b297743c82c20f947053ee667dd Every other ticker on this chain launches with a promise. This one launches with 400 receipts. What is already running while you read this: > An agent reading all 24 Robinhood Stock Tokens against the real share price every 20 seconds. > A line at 1.0% while the real NYSE is open, 1.5% the moment the bell rings and the oracle stops moving. > A scorecard with 386 hits, 14 misses and 2 calls open right now, nothing deleted, nothing hidden. The real NYSE opens in thirty minutes. The desk tightens its own line the second it does. Every call lands in Telegram before it lands anywhere else, the board is public and free, and the code is open with no wallet in it. There is a room on Wall Street that does this with forty people and a data bill with a comma in it. Mine is one agent, one laptop and now one ticker. Holders get the faster board, the earlier alerts and the agent's own stops, in that order. $NYSE SEE YOU AFTER THE BELL ↓
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THE DESK RAN SIX DAYS FOR FREE AND CLOSED 96% OF 400 CALLS BEFORE IT HAD A TICKER -> TODAY IT GETS ONE $NYSE IS LIVE ON ROBINHOOD CHAIN, BUILT WITH MINARA AI AND GPT-6 ASTRA, AND THE SCORECARD IS OLDER THAN THE CHART CA: 0x3cedefe814e47b297743c82c20f947053ee667dd Every other ticker on this chain launches with a promise. This one launches with 400 receipts. What is already running while you read this: > An agent reading all 24 Robinhood Stock Tokens against the real share price every 20 seconds. > A line at 1.0% while the real NYSE is open, 1.5% the moment the bell rings and the oracle stops moving. > A scorecard with 386 hits, 14 misses and 2 calls open right now, nothing deleted, nothing hidden. The real NYSE opens in thirty minutes. The desk tightens its own line the second it does. Every call lands in Telegram before it lands anywhere else, the board is public and free, and the code is open with no wallet in it. There is a room on Wall Street that does this with forty people and a data bill with a comma in it. Mine is one agent, one laptop and now one ticker. Holders get the faster board, the earlier alerts and the agent's own stops, in that order. $NYSE SEE YOU AFTER THE BELL ↓

slash1s

59,786 görüntüleme • 1 ay önce

Opus 5.5 just filed a pull request that changes the streaming behavior in T3 code. It's so cool that I can send off a prompt with a vague idea of what I want, and the result is a ready-to-merge pull request with a video demo of the changes directly in the PR. The PRs I make with AI are significantly better than the ones I used to make by hand. The future is awesome.
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Opus 5.5 just filed a pull request that changes the streaming behavior in T3 code. It's so cool that I can send off a prompt with a vague idea of what I want, and the result is a ready-to-merge pull request with a video demo of the changes directly in the PR. The PRs I make with AI are significantly better than the ones I used to make by hand. The future is awesome.

Theo - t3.gg

94,590 görüntüleme • 1 ay önce

THIS IS WHAT A SUPPORT TEAM LOOKS LIKE IN 2026 A customer reports that their export stops at 10,000 of 18,400 contacts. And instead of passing the ticket from tool to tool: → Support agent replies → Coding agent finds the bug → Docs agent updates the guide → CRM agent prepares the renewal All on the same ticket, with the same context: logs, billing, code and previous conversations. It's called Helpin. It's open source and you can self-host it.
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THIS IS WHAT A SUPPORT TEAM LOOKS LIKE IN 2026 A customer reports that their export stops at 10,000 of 18,400 contacts. And instead of passing the ticket from tool to tool: → Support agent replies → Coding agent finds the bug → Docs agent updates the guide → CRM agent prepares the renewal All on the same ticket, with the same context: logs, billing, code and previous conversations. It's called Helpin. It's open source and you can self-host it.

marcus

22,919 görüntüleme • 29 gün önce

🚨 NOW YOU RUN A COMPANY WITH ZERO EMPLOYEES Paperclip is a 100% open-source framework (70k+ stars) that makes this possible. Rather than just prompting a model, you hire a CEO, engineers, and a QA reviewer. Every worker is an AI agent, and Paperclip is the Node.js and React control plane that keeps them aligned. Stop chaining messy scripts together and build a living organization: → Stand up a CEO agent to set strategy → Hire engineers and designers via Claude or Codex → Build in an automated QA loop before any ticket closes → Manage the entire portfolio from your phone When an agent slips, you do not rewrite your whole pipeline: you just correct its persona prompt, exactly like coaching a junior hire. It is exactly the kind of tooling the space needs right now. Free, open-source, and self-hosted. Repo link in 🧵↓
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🚨 NOW YOU RUN A COMPANY WITH ZERO EMPLOYEES Paperclip is a 100% open-source framework (70k+ stars) that makes this possible. Rather than just prompting a model, you hire a CEO, engineers, and a QA reviewer. Every worker is an AI agent, and Paperclip is the Node.js and React control plane that keeps them aligned. Stop chaining messy scripts together and build a living organization: → Stand up a CEO agent to set strategy → Hire engineers and designers via Claude or Codex → Build in an automated QA loop before any ticket closes → Manage the entire portfolio from your phone When an agent slips, you do not rewrite your whole pipeline: you just correct its persona prompt, exactly like coaching a junior hire. It is exactly the kind of tooling the space needs right now. Free, open-source, and self-hosted. Repo link in 🧵↓

Charly Wargnier

37,174 görüntüleme • 4 ay önce

✜ Setting up the workspace is the next important step in the setup. ✜ You should open the indicator's source code and click "Create a working copy" ✜ Come up with a name for your workspace and fill in the field.
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✜ Setting up the workspace is the next important step in the setup. ✜ You should open the indicator's source code and click "Create a working copy" ✜ Come up with a name for your workspace and fill in the field.

ᴛʀᴀᴄᴇʀ

28,026 görüntüleme • 2 yıl önce

Nike would pay you $3000 for this. Took me some 15 minutes to set up a Claude skill using Seedance 2.0 and Arcads. This is how it works: → I send a product image to Claude → Claude analyzes the product → Writes an ad script → Triggers arcads api with image and prompt → Arcads generates a product ad → Claude updates my Notion database RT + comment "AGENT" and I'll send you the skills .md, full setup, and a guide for free.
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Nike would pay you $3000 for this. Took me some 15 minutes to set up a Claude skill using Seedance 2.0 and Arcads. This is how it works: → I send a product image to Claude → Claude analyzes the product → Writes an ad script → Triggers arcads api with image and prompt → Arcads generates a product ad → Claude updates my Notion database RT + comment "AGENT" and I'll send you the skills .md, full setup, and a guide for free.

Kritarth Mittal | Soshals

12,317 görüntüleme • 7 ay önce

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 görüntüleme • 8 ay önce

Turn your Cursor agent window into a deck builder. Composer 2.5 + open-slide is a great combo - the model handles design and generation cleanly, and the browser integration lets you watch the deck come together live as the agent works. $ 𝚗𝚙𝚡 @𝚘𝚙𝚎𝚗-𝚜𝚕𝚒𝚍𝚎/𝚌𝚕𝚒 𝚒𝚗𝚒𝚝
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Turn your Cursor agent window into a deck builder. Composer 2.5 + open-slide is a great combo - the model handles design and generation cleanly, and the browser integration lets you watch the deck come together live as the agent works. $ 𝚗𝚙𝚡 @𝚘𝚙𝚎𝚗-𝚜𝚕𝚒𝚍𝚎/𝚌𝚕𝚒 𝚒𝚗𝚒𝚝

Yiwei Ho

16,541 görüntüleme • 5 ay önce

My least favorite AI video game: watching an amazing clip and spending 40 minutes guessing the prompt. Dreamina has a better answer. Prompt tools hand you a clip you can't control. A canvas hands you a project you can. Dreamina 2.0 is canvas-first — the new way AI video is made. #Dreamina #DreaminaPartner
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My least favorite AI video game: watching an amazing clip and spending 40 minutes guessing the prompt. Dreamina has a better answer. Prompt tools hand you a clip you can't control. A canvas hands you a project you can. Dreamina 2.0 is canvas-first — the new way AI video is made. #Dreamina #DreaminaPartner

FELIX

67,191 görüntüleme • 1 ay önce

Found a site that collects 𝟮𝟯𝟬+ 𝗺𝗼𝘁𝗶𝗼𝗻 𝘃𝗶𝗱𝗲𝗼𝘀 made with Opus 5.5, and every video comes with 𝘁𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝗼𝗿 𝘀𝗸𝗶𝗹𝗹 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝗶𝘁. Each one has a video preview, the full prompt or skill, one-click copy, the author with a link to their original post, and the model used. There are product launch videos, app promos, personal showreels, kinetic type and explainer animations. Pick one you like, paste its prompt into Opus 5.5 and swap in your own product. The whole prompt for the one in the video is 𝗷𝘂𝘀𝘁 𝘁𝘄𝗼 𝘀𝗲𝗻𝘁𝗲𝗻𝗰𝗲𝘀.
0:15

Found a site that collects 𝟮𝟯𝟬+ 𝗺𝗼𝘁𝗶𝗼𝗻 𝘃𝗶𝗱𝗲𝗼𝘀 made with Opus 5.5, and every video comes with 𝘁𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝗼𝗿 𝘀𝗸𝗶𝗹𝗹 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝗶𝘁. Each one has a video preview, the full prompt or skill, one-click copy, the author with a link to their original post, and the model used. There are product launch videos, app promos, personal showreels, kinetic type and explainer animations. Pick one you like, paste its prompt into Opus 5.5 and swap in your own product. The whole prompt for the one in the video is 𝗷𝘂𝘀𝘁 𝘁𝘄𝗼 𝘀𝗲𝗻𝘁𝗲𝗻𝗰𝗲𝘀.

Vox

35,075 görüntüleme • 29 gün önce

we just released a new blog "Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv" you can take a real coding agent (OpenCode), let it run its own tool loop against real coding problems, and train it with RL on the exact tokens it produced and every rollout runs in its own remote HF sandbox, so rollouts scale out beyond one machine the loop: - OpenCode owns its tool loop inside an OpenEnv sandbox - an in-sandbox proxy records the real token ids + logprobs, per turn - a hidden-test verifier scores the result, and that is the reward - TRL trains with AsyncGRPO, weights sync back to vLLM over NCCL blog + runnable example:
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we just released a new blog "Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv" you can take a real coding agent (OpenCode), let it run its own tool loop against real coding problems, and train it with RL on the exact tokens it produced and every rollout runs in its own remote HF sandbox, so rollouts scale out beyond one machine the loop: - OpenCode owns its tool loop inside an OpenEnv sandbox - an in-sandbox proxy records the real token ids + logprobs, per turn - a hidden-test verifier scores the result, and that is the reward - TRL trains with AsyncGRPO, weights sync back to vLLM over NCCL blog + runnable example:

Sergio Paniego

38,088 görüntüleme • 3 ay önce

best coding agent use case for Jev: enforcing rules a linter can't coding agents tend to break rules and some rules can't be codified Jev can now score every turn against such rules and tell the agent what to fix asap open-source:
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best coding agent use case for Jev: enforcing rules a linter can't coding agents tend to break rules and some rules can't be codified Jev can now score every turn against such rules and tell the agent what to fix asap open-source:

Ohans Emmanuel

118,833 görüntüleme • 1 ay önce

this is f*cking gold Google engineers explained how to make AI rewrite an agent’s system prompt against failing tests. a failed check becomes the next repair job. the regression suite watches for whatever that repair breaks. even the instructions become something you can test and improve. Agent = Model + Harness. the independently compiled page here maps the broader system into six parts: guides carry project rules, constraints and lessons from past failures > sensors check the work through tests, linters and validators > the loop runs the task, checks the result, retries within limits and escalates > memory preserves state, artifacts and decisions across runs > permissions control tools, writes and actions requiring approval > observability records what happened, what it cost and where it failed an agent changes a build file and announces “done.” the check looks for evidence that it actually ran the validator. if that behavior is missing, you have a specific failure to target. adjust the instructions. rerun the evaluations. check whether the improvement holds without breaking existing behavior. now a prompt change has a test history. a recurring mistake has a regression check. the next model upgrade has something concrete to pass. bookmark the diagram. give “done” a test it has to pass.
0:17

this is f*cking gold Google engineers explained how to make AI rewrite an agent’s system prompt against failing tests. a failed check becomes the next repair job. the regression suite watches for whatever that repair breaks. even the instructions become something you can test and improve. Agent = Model + Harness. the independently compiled page here maps the broader system into six parts: guides carry project rules, constraints and lessons from past failures > sensors check the work through tests, linters and validators > the loop runs the task, checks the result, retries within limits and escalates > memory preserves state, artifacts and decisions across runs > permissions control tools, writes and actions requiring approval > observability records what happened, what it cost and where it failed an agent changes a build file and announces “done.” the check looks for evidence that it actually ran the validator. if that behavior is missing, you have a specific failure to target. adjust the instructions. rerun the evaluations. check whether the improvement holds without breaking existing behavior. now a prompt change has a test history. a recurring mistake has a regression check. the next model upgrade has something concrete to pass. bookmark the diagram. give “done” a test it has to pass.

NO1ennn

72,154 görüntüleme • 1 ay önce

It's true, most AI coding tools make you do the grunt testing and debugging work. That's why we invested in researching and building a state-of-the-art app testing tool. Replit Agent 3 will spin up a browser and test your app end-to-end.
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It's true, most AI coding tools make you do the grunt testing and debugging work. That's why we invested in researching and building a state-of-the-art app testing tool. Replit Agent 3 will spin up a browser and test your app end-to-end.

Amjad Masad

110,113 görüntüleme • 11 ay önce

GPT-6 Astra test 4/n Prompt: "Implement pen spinning with a dexterous hand. Use Isaac Lab for RL training, use the Sharpa hand, and create the pen mesh yourself. Give me a trained RL policy and a visualization video. You are free to search the web and download papers or anything else you need." After running autonomously for a day and a half (including training the policy), this is what we got: Credit: my student Chengyang Li
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GPT-6 Astra test 4/n Prompt: "Implement pen spinning with a dexterous hand. Use Isaac Lab for RL training, use the Sharpa hand, and create the pen mesh yourself. Give me a trained RL policy and a visualization video. You are free to search the web and download papers or anything else you need." After running autonomously for a day and a half (including training the policy), this is what we got: Credit: my student Chengyang Li

Wentao Zhu

695,884 görüntüleme • 1 ay önce