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Don't make your agent hunt for the right file. Use Cmd+L to highlight a specific block and send it straight to the Antigravity Agent chat prompt as a context item. Watch as we turn a simple list of flavors into 12 with zero distractions, just direct revision. Clean context... show more

Google Antigravity

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81,074 görüntüleme • 5 ay önce •via X (Twitter)

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Stop repeating yourself and start building skills. 🛠️ In Antigravity, creating new agent skills is as simple as a single prompt. Watch how the agent captures repetitive coding actions.

Stop repeating yourself and start building skills. 🛠️ In Antigravity, creating new agent skills is as simple as a single prompt. Watch how the agent captures repetitive coding actions.

Google Antigravity

208,706 görüntüleme • 7 ay önce

With Google Antigravity, getting familiar with a new codebase is as simple as asking the right questions. 🎯Highlight any block of code to ask specific questions. 🔗Drag-and-drop multiple files together to instantly see how they interact. 🧜‍♀️Ask for a Mermaid diagram to map out the architecture.

With Google Antigravity, getting familiar with a new codebase is as simple as asking the right questions. 🎯Highlight any block of code to ask specific questions. 🔗Drag-and-drop multiple files together to instantly see how they interact. 🧜‍♀️Ask for a Mermaid diagram to map out the architecture.

Google Antigravity

108,852 görüntüleme • 7 ay önce

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

Jerry Liu

21,073 görüntüleme • 7 ay önce

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,166 görüntüleme • 3 ay önce

Introducing PAI 1.0: The First Version of Personal Super Intelligence PAI is the world's first context-aware, widget based AI agent that floats over your screen to read your context in real time and proactively suggest next steps, ensuring safety by executing actions only with your explicit approval. The floating AI interface designed to eliminate the prompt inequality and barrier. Structurally eliminating the "navigate to a chat window, write a prompt, explain your context" workflow that existing AI services have treated as a given for years.

Introducing PAI 1.0: The First Version of Personal Super Intelligence PAI is the world's first context-aware, widget based AI agent that floats over your screen to read your context in real time and proactively suggest next steps, ensuring safety by executing actions only with your explicit approval. The floating AI interface designed to eliminate the prompt inequality and barrier. Structurally eliminating the "navigate to a chat window, write a prompt, explain your context" workflow that existing AI services have treated as a given for years.

Eon (Yulheon) Seung

131,244 görüntüleme • 2 ay önce

Introducing Inspector's context-aware screenshot tool! Screenshots as context for your coding agent is a pain. I decided I was tired of it and shipped something today to help with that.

Introducing Inspector's context-aware screenshot tool! Screenshots as context for your coding agent is a pain. I decided I was tired of it and shipped something today to help with that.

mikeBuildsMore

21,965 görüntüleme • 9 ay önce

We just added Prompt Completions to Augment for VS Code. You’ll now get context-aware suggestions as you type prompts for your agent — including natural-language completions, variable names, file references, and more. It makes writing great prompts faster, especially for users who craft long specs or detailed instructions. Powered by full codebase context and agent history, so agent prompt completions feel in sync with what users are trying to write.

We just added Prompt Completions to Augment for VS Code. You’ll now get context-aware suggestions as you type prompts for your agent — including natural-language completions, variable names, file references, and more. It makes writing great prompts faster, especially for users who craft long specs or detailed instructions. Powered by full codebase context and agent history, so agent prompt completions feel in sync with what users are trying to write.

Augment Code

593,917 görüntüleme • 9 ay önce

🚀 We just shipped a major update to LangSmith Agent Builder: • New agent chat: One always-available agent with access to all your workspace tools • Chat → Agent: Turn any conversation into a specialized agent with one click • File uploads: Attach files directly to Agent Builder • Tool registry: Add, authenticate, and manage your tools in one place Try it now: Learn more:

🚀 We just shipped a major update to LangSmith Agent Builder: • New agent chat: One always-available agent with access to all your workspace tools • Chat → Agent: Turn any conversation into a specialized agent with one click • File uploads: Attach files directly to Agent Builder • Tool registry: Add, authenticate, and manage your tools in one place Try it now: Learn more:

LangChain

24,885,978 görüntüleme • 6 ay önce

This is Repo Prompt 1.5 The new Context Builder connects to your agent of choice, using your existing subscriptions, to fully automate the process of building the perfect context for a given token budget. All of Repo Prompt's power, fully automated, with 1 click

This is Repo Prompt 1.5 The new Context Builder connects to your agent of choice, using your existing subscriptions, to fully automate the process of building the perfect context for a given token budget. All of Repo Prompt's power, fully automated, with 1 click

eric provencher

195,327 görüntüleme • 10 ay önce

we just used Replit ⠕ agent to build a browser agent 🤯 you might not want to give OpenAI your $200 just yet... this cost $6.14 to build. here it is browsing Product Hunt 😸 and making a list from a prompt...

we just used Replit ⠕ agent to build a browser agent 🤯 you might not want to give OpenAI your $200 just yet... this cost $6.14 to build. here it is browsing Product Hunt 😸 and making a list from a prompt...

MakerThrive

274,418 görüntüleme • 1 yıl önce

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below

Morlex

50,478 görüntüleme • 14 gün önce

In Cursor you can *easily* turn comments into code. Just write in natural language what you want in your file. Then highlight it, use CMD+K, and ask the AI to translate it into code - and it will! Turn your thoughts right into apps.

In Cursor you can easily turn comments into code. Just write in natural language what you want in your file. Then highlight it, use CMD+K, and ask the AI to translate it into code - and it will! Turn your thoughts right into apps.

Mckay Wrigley

106,485 görüntüleme • 2 yıl önce

In the era of long-context LLMs, I needed a way to quickly convert a GitHub repo into a text file to feed as a prompt. Introducing RepoToTextForLLMs 📖 Not only does it return the full repo in one file, but it also appends a super prompt for analysis and understanding 👇

In the era of long-context LLMs, I needed a way to quickly convert a GitHub repo into a text file to feed as a prompt. Introducing RepoToTextForLLMs 📖 Not only does it return the full repo in one file, but it also appends a super prompt for analysis and understanding 👇

Pietro Schirano

104,922 görüntüleme • 2 yıl önce

Check out the form-filling agent that automates PDF forms using AI by Jerry Liu 📄🤖 Use any fillable PDF with an agent that fills it out based on your prompts and context files. Our new experiment creates a multi-turn chat experience for form completion. 🔍 Upload fillable PDFs and automatically detect form fields using PyMuPDF 📝 Add custom prompts and context files (parsed via LlamaParse) to guide the AI 🤖 Multi-turn conversations let you refine and correct form entries after initial completion 💾 Download your completed forms when done The agent uses simple tools to list, set, get, and validate form fields. You can chat with it to make corrections and adjustments until your form is perfect. Check out the code on GitHub: Or the deployed app here:

Check out the form-filling agent that automates PDF forms using AI by Jerry Liu 📄🤖 Use any fillable PDF with an agent that fills it out based on your prompts and context files. Our new experiment creates a multi-turn chat experience for form completion. 🔍 Upload fillable PDFs and automatically detect form fields using PyMuPDF 📝 Add custom prompts and context files (parsed via LlamaParse) to guide the AI 🤖 Multi-turn conversations let you refine and correct form entries after initial completion 💾 Download your completed forms when done The agent uses simple tools to list, set, get, and validate form fields. You can chat with it to make corrections and adjustments until your form is perfect. Check out the code on GitHub: Or the deployed app here:

LlamaIndex 🦙

55,131 görüntüleme • 7 ay önce

Hgraph AI Data Agent ✨ Now Live! Instantly chat with @Hedera data in your browser for free using our new data agent. Meet the network explorer that explores for you. Send a prompt → Watch now fast it is...

Hgraph AI Data Agent ✨ Now Live! Instantly chat with @Hedera data in your browser for free using our new data agent. Meet the network explorer that explores for you. Send a prompt → Watch now fast it is...

Hgraph

25,210 görüntüleme • 8 ay önce

Claude Code just shipped v2.1.69 with a new built-in skill: /claude-api It detects your language, picks the right surface (direct API, tool use, or Agent SDK), and loads the relevant reference docs directly into context. Covers streaming, batches, files API, structured outputs, tool use patterns, MCP, also sets model defaults so you're not guessing. Useful when you're building on top of Claude and don't want to context-switch to the docs every 5 minutes.

Claude Code just shipped v2.1.69 with a new built-in skill: /claude-api It detects your language, picks the right surface (direct API, tool use, or Agent SDK), and loads the relevant reference docs directly into context. Covers streaming, batches, files API, structured outputs, tool use patterns, MCP, also sets model defaults so you're not guessing. Useful when you're building on top of Claude and don't want to context-switch to the docs every 5 minutes.

Daniel San

152,831 görüntüleme • 5 ay önce

“I’ve never looked at a skill file once.” From Riley Brown: “Ask AI to do a thing using a skill. If it doesn’t do a good job, tell it to change the skill so it doesn’t make that mistake again. Then test the agent in a new chat with clean context.” “Hold your AI to a standard, then improve the skill by measuring the outcomes.” 📌 Watch the full episode here:

“I’ve never looked at a skill file once.” From Riley Brown: “Ask AI to do a thing using a skill. If it doesn’t do a good job, tell it to change the skill so it doesn’t make that mistake again. Then test the agent in a new chat with clean context.” “Hold your AI to a standard, then improve the skill by measuring the outcomes.” 📌 Watch the full episode here:

Peter Yang

21,572 görüntüleme • 13 gün önce

This is Repo Prompt 2.0 A fully integrated agent, that makes it seamless to use RP's powerful MCP tools, with a built-in oracle and context builder. A first class experience showcasing how much better and efficient your agents can be with good context engineering tools.

This is Repo Prompt 2.0 A fully integrated agent, that makes it seamless to use RP's powerful MCP tools, with a built-in oracle and context builder. A first class experience showcasing how much better and efficient your agents can be with good context engineering tools.

eric provencher

38,921 görüntüleme • 6 ay önce

This is actually useful. LangChain just released OpenWiki. It's an open-source agent that creates a wiki for your codebase, connects it to your coding agent, and keeps it updated as your repo changes. Your AI coding agent gets long-term repo context without stuffing everything into CLAUDE.md. Here's how to setup. Save this.

This is actually useful. LangChain just released OpenWiki. It's an open-source agent that creates a wiki for your codebase, connects it to your coding agent, and keeps it updated as your repo changes. Your AI coding agent gets long-term repo context without stuffing everything into CLAUDE.md. Here's how to setup. Save this.

Min Choi

125,815 görüntüleme • 1 ay önce

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,598 görüntüleme • 2 ay önce