3/ we’re also releasing contemplating mode, which orchestrates multiple agents that reason in parallel designed to handle complex scientific & reasoning queries. in our testing we found it competitive w/ other extreme reasoning models such as Gemini Deep Think & GPT Pro.
TOOLS
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Deploy Multi-Agent Systems with A2A Protocol
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🔌 Deploy agents with A2A
— LangChain (@LangChain) 8 avril 2026
A2A is an agent-to-agent communication protocol, useful for building multi-agent systems. With LangSmith Deployments, you get A2A support out of the box!
Watch how: https://t.co/PF2FZbOb79
Docs: https://t.co/ETQ24g3KOX
A2A Protocol:… pic.twitter.com/TJVX5v9RZu🔌 Deploy agents with A2A A2A is an agent-to-agent communication protocol, useful for building multi-agent systems. With LangSmith Deployments, you get A2A support out of the box! Watch how: piped.video/SjGPXBNH614 Docs: docs.langchain.com/langsmith… A2A Protocol: a2a-protocol.org/latest/
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Meta.ai platform now available for checkout
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Meta.ai is where you can check it out
→ View original post on X — @kimmonismus, 2026-04-08 15:53 UTC
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Google’s TurboQuant: Have You Tried This Technology?
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Interesting have you tried googles turboquant?
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AI Cow Collar Technology Creates Billion Dollar Agricultural Innovation
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An AI cow collar just created a billion-dollar company.
— Rowan Cheung (@rowancheung) 8 avril 2026
Farmers draw boundaries on a phone app, and the collars guide cows using sound and vibration.
It works by collecting over 6,000 data points per min, feeding ML models that track grazing patterns, predict disease, and… pic.twitter.com/nZpftENM2HAn AI cow collar just created a billion-dollar company. Farmers draw boundaries on a phone app, and the collars guide cows using sound and vibration. It works by collecting over 6,000 data points per min, feeding ML models that track grazing patterns, predict disease, and
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Avatar V: Revolutionary AI Character Consistency Technology Unveiled
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Introducing Avatar V. We’ve solved character consistency. Forever.
— Joshua Xu (@joshua_xu_) 8 avril 2026
Record yourself once for 15 seconds. From there, you can show up anywhere, in any look, and it still feels like you. Any photo becomes a video that looks, moves, and speaks like you, down to your mannerisms and… pic.twitter.com/qQsWlRoOkwIntroducing Avatar V. We’ve solved character consistency. Forever. Record yourself once for 15 seconds. From there, you can show up anywhere, in any look, and it still feels like you. Any photo becomes a video that looks, moves, and speaks like you, down to your mannerisms and quirks. This is the most advanced AI avatar model in the world. And we know that’s a big claim, so we brought the data to prove it. Thread below:
→ View original post on X — @aihighlight, 2026-04-08 15:01 UTC
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Avatar V: Revolutionary Character Consistency in 15 Seconds
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We solved character consistency. Forever
— HeyGen (@HeyGen) 8 avril 2026
Avatar V captures you in 15 seconds and holds your identity across every video.
Change the look, outfit, and setting to create unlimited versions of you.
RT + comment "AvatarV" below and I'll DM 100 credits to test it out (must follow) pic.twitter.com/el5kNz4IOdWe solved character consistency. Forever Avatar V captures you in 15 seconds and holds your identity across every video. Change the look, outfit, and setting to create unlimited versions of you. RT + comment "AvatarV" below and I'll DM 100 credits to test it out (must follow) [Translated from EN to English]
→ View original post on X — @scobleizer, 2026-04-08 15:00 UTC
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Intelligent Agents: Natural Language Automation Across Systems
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Enter intelligent agents (the practical layer): Ask in natural language. The agent pulls context across systems, then acts—send an alert, generate a report, flag an anomaly. Result → faster cycles, clearer visibility, and automation you can trust across existing infrastructure.
→ View original post on X — @ronald_vanloon, 2026-04-08 15:00 UTC
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Context Engineering: Organizing Unstructured Data for AI Reasoning
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What is “Context Engineering”? It’s the discipline of stitching your unstructured mess—logs, chats, docs, images—into something an AI can actually reason over. → Vector DBs + hybrid search + embeddings to retrieve by meaning, not keywords. → Decisions anchored in your data, not generic pretraining.
→ View original post on X — @ronald_vanloon, 2026-04-08 15:00 UTC
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Using LLMs for Automated Bug Generalization and Code Analysis
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As one non-limiting example, a sufficiently motivated engineer could, as a hobby project, build a pipeline to reverse engineer post-Mythos patches then describe the bugs found in markdown and tell a less-creative LLM “Generalize from this data set please against this code base.”