260313 X(트위터) 모음
[10/10] We built a CLI to help parse and search over your PDFs, with best-in-class accur
We built a CLI to help parse and search over your PDFs, with best-in-class accuracy compared to other OCR tools.
You can run it by itself, or plug it as a skill into Claude Code / OpenClaw / any gene
We built a CLI to help parse and search over your PDFs, with best-in-class accuracy compared to other OCR tools.
You can run it by itself, or plug it as a skill into Claude Code / OpenClaw / any generalized agent.
Repo: github.com/run-llama/semtool…
Uses LlamaParse: cloud.llamaindex.ai/?utm_sou…
LlamaIndex 🦙 (@llama_index)
🔎 semtools v3.0.0 is out, and it's a great step forward for anyone using semantic search and document parsing from the command line.
For context: semtools is our Rust-based CLI that lets you parse documents (PDFs, DOCX, PPTX, and more) via LlamaParse, run fast local semantic search using multilingual embeddings, and ask questions over document collections using an AI agent — all from your terminal.
Here's what changed in v3.0.0:
🤝 Unified interface. All commands now live under a single semtools entry point — parse, search, ask, and workspace. Much more discoverable, much easier to document.
✅ --json output on every command. This was one of the most requested features. Structured JSON output means you can pipe semtools into jq, embed it in shell scripts, or use it as a tool inside a coding agent. This kind of composability is what makes CLI tools genuinely useful beyond interactive sessions.
🐜 Dramatically smaller binary. The storage layer was migrated to @qdrant_engine Edge — a lightweight, edge-optimized vector database — and the binary size dropped from multiple gigabytes to a few hundred megabytes. Same functionality, much lighter footprint, easier to install and distribute.
💻 --workspace CLI flag. You can now specify a workspace directly on the command line instead of relying on environment variables. A small change with a big improvement to day-to-day ergonomics.
agent_orchestration ron agent claude code openclaw
출처: https://github.com/run-llama/semtools">github.com/run-llama/semtool…<br
점수: 10/10 — 점수 10/10: claude code, openclaw, claude
[9/10] New course: A2A: The Agent2Agent Protocol, built with @googlecloudtech and @IBM
New course: A2A: The Agent2Agent Protocol, built with @googlecloudtech and @IBMResearch, and taught by Holt Skinner, @ivnardini, and Sandi Besen.
Connecting agents built with different frameworks us
New course: A2A: The Agent2Agent Protocol, built with @googlecloudtech and @IBMResearch, and taught by Holt Skinner, @ivnardini, and Sandi Besen.
Connecting agents built with different frameworks usually requires extensive custom integration. This short course teaches you A2A, the open protocol standardizing how agents discover each other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard.
In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows.
Skills you'll gain:
- Expose agents from different frameworks as A2A servers to make them discoverable and interoperable
- Chain A2A agents sequentially using ADK, where one agent's output feeds into the next
- Connect A2A agents to external data sources using MCP (Model Context Protocol)
- Deploy A2A agents using Agent Stack, IBM's open-source infrastructure
Join and learn the protocol standardizing agent collaboration!
deeplearning.ai/short-course…
Video
agent_orchestration orches agent multi-agent agent collaboration model context protocol
출처: https://nitter.net/googlecloudtech"
점수: 9/10 — 점수 9/10: mcp, model context protocol, multi-agent
[6/10] RT by @hwchase17: The @LangChain crew (@hwchase17 @Vtrivedy10) is the only crew
RT by @hwchase17: The @LangChain crew (@hwchase17 @Vtrivedy10) is the only crew that rivals @AnthropicAI in consistently sharing practical pearls of Agentic wisdom
The @LangChain crew (@hwchase17 @Vtrivedy10) is the only crew that rivals @AnthropicAI in consistently sharing practical pearls of Agentic wisdom
Rajan Rengasamy (@cmd_alt_ecs)
essential reading from this week:
@Vtrivedy10 — "The Anatomy of an Agent Harness" (215K views)
@nyk_builderz — "The Harness Is The Product" (35K views)
@hwchase17 — "How Coding Agents Are Reshaping EPD" (508K views)
if you're building agents and haven't read these, you're solving the wrong problem.
agent_orchestration ron agent
출처: https://nitter.net/LangChain"
점수: 6/10 — 점수 6/10: anthropic
[6/10] R to @AnthropicAI: The Institute will be led by @jackclarkSF, in a new role as A
R to @AnthropicAI: The Institute will be led by @jackclarkSF, in a new role as Anthropic’s Head of Public Benefit. It'll bring together an interdisciplinary staff of machine learning engineers, econom
The Institute will be led by @jackclarkSF, in a new role as Anthropic’s Head of Public Benefit. It'll bring together an interdisciplinary staff of machine learning engineers, economists, and social scientists, making full use of the inside information of a frontier AI lab.
agent_orchestration ron출처: https://nitter.net/jackclarkSF"
점수: 6/10 — 점수 6/10: anthropic
[8/10] RT by @hwchase17: 🔥 Qodo beats Claude Code Review by 19% and is 10× cheaper We
RT by @hwchase17: 🔥 Qodo beats Claude Code Review by 19% and is 10× cheaper
We ran a head-to-head test between the two.
Precision was identical for Qodo and Claude. But Qodo achieved 19% higher reca
🔥 Qodo beats Claude Code Review by 19% and is 10× cheaper
We ran a head-to-head test between the two.
Precision was identical for Qodo and Claude.
But Qodo achieved 19% higher recall and a 12% higher F1 score.
That’s a huge gap.
And with Qodo, you also won’t go broke (Claude charges ~$20 per PR).
Because in practice, what matters most is actually catching the real issues in code, especially if you aren’t planning to read every line of a PR.
So if you want to catch everything, the choice becomes pretty clear.
One broader takeaway:
The best code generator isn’t necessarily the best code reviewer.
This is something we realized a while back, which is why the results above weren’t surprising to us.
Code review should be independent from your code generator.
Full breakdown:
qodo.ai/blog/qodo-outperform…
출처: https://www.qodo.ai/blog/qodo-outperforms-claude-in-code-review-benchmark/">qodo.ai/blog/qodo-outperform…
점수: 8/10 — 점수 8/10: claude code, claude
[6/10] RT by @hwchase17: few takeaways from @Daytonaio’s Compute conference: - we are
RT by @hwchase17: few takeaways from @Daytonaio’s Compute conference:
-
we are way past the “agent” hype, harnesses are the new agents (@hwchase17)
-
build tools for agents, memory, computers (sandb
few takeaways from @Daytonaio’s Compute conference:
- we are way past the “agent” hype, harnesses are the new agents (@hwchase17)
- build tools for agents, memory, computers (sandboxes), infra… those are your new customers, the whole AWS stack will be rebuilt for agents (@paraga)
- soon we’ll be looking at IAP (Ideal Agent Profile) in addition fo ICP and we’ll have new metrics such as DAA and MAA (my observation)
- new-old business models are becoming viable (e.g. agents paying 10 cents to access specific resources) (@levie)
- memory remains unsolved (chase)
- sandboxes are becoming more versatile with long term memory, access to more OSs (Daytona’s Windows + Android demos were amazing) and soon every agent will have a computer (@JukicVedran)
- search is a big deal both internet and intranet (@p0)
- lot of MCP talk although I think MCP is going away and pure API’s with SKILLmd files will take over (was never a big fan of MCP anyways)
- Knowledge work will be done via code whether you know it or not (an agent codes on the fly on your behalf) (on it with @nocodeinc)
- Identity + permissions remain unsolved too (agents acting on your behalf or as a separate entity, and to what extent)
agent_orchestration llm hyp ron agent
출처: https://nitter.net/Daytonaio"
점수: 6/10 — 점수 6/10: mcp
[6/10] RT by @hwchase17: Everything Gets Rebuilt: my conversation with Harrison Chase,
RT by @hwchase17: Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of @LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack
00:00 Intro
Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of @LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack
00:00 Intro - meet @hwchase17 - at the Chase Center for the @daytonaio Compute conference
01:32 What changed in agents over the last year
03:57 Why coding agents are ahead
06:26 Do models commoditize the framework layer?
08:27 Harnesses, in plain English
10:11 Why system prompts matter so much
13:11 The upside — and downside — of subagents
15:31 Why a useful agent needs a filesystem
18:13 Additional core primitives of modern agents
19:12 Skills: the new primitive
20:19 What context compaction actually means
23:02 How memory works in agents
25:16 One mega-agent or many specialized agents?
27:46 The future of MCP
29:38 Why agents need sandboxes
32:35 How sandboxes help with security
33:32 How Harrison Chase started LangChain
37:24 LangChain vs LangGraph vs Deep Agents
40:17 Why observability matters more for agents
41:48 Evals, no-code, and continuous improvement
44:41 What LangChain is building next
45:29 Where the real moat in AI lives
Video
agent_orchestration agent
출처: https://nitter.net/LangChain"
점수: 6/10 — 점수 6/10: mcp
[6/10] RT by @hwchase17: @hwchase17 said "harnesses are the new agents" at the Compute
RT by @hwchase17: @hwchase17 said "harnesses are the new agents" at the Compute conference and I felt that.
The model matters less than where it works. http://monday.com just opened its platform with
@hwchase17 said "harnesses are the new agents" at the Compute conference and I felt that.
The model matters less than where it works. monday.com just opened its platform with MCP support, full API, and skill files - so any LangChain agent can plug into a real team. Free.
출처: http://monday.com
점수: 6/10 — 점수 6/10: mcp