6/6 This is the core takeaway: With structured plans and decision-theoretic optimization techniques, you can get a lot more with the same models, tools and compute. And as you scale, the gap widens.
AGENTS
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Structured Execution: Selective Scaling for Efficient Agent Workflows
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5/6 With structured execution, execution is controlled and stays on track. You can scale only individual steps that will benefit from it, instead of doing it for the entire agentic trajectory.
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ReAct Agents and High-Variance Execution in Long-Horizon Tasks
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4/6 ReAct-style agents encode the entire plan in text, and only get evaluated at the very end. In long-horizon tasks, that turns execution into a high-variance gamble.
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Maestro: Optimizing Compute Through Agentic Framework Structure
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2/6 SWE-bench is not the story. Maestro is not a dedicated SWE agent, it’s an agentic framework that optimizes based on structure and learned priors. ex: knowing that 4 runs of GPT-5 mini are cheaper and better than one GPT-5 run, leads to better use of the same compute –
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Orchestrated Test-Time Compute Scaling for Long-Horizon Agentic Tasks
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1/6 Long-horizon agentic tasks are breaking our mental models. More tokens, bigger models, and best-of-N only go so far. Orchestrated approach to test-time-compute scaling is what long-horizon tasks need. Here’s what we learned using SWE-bench as a test case. Read the blog for
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Lakebase: Managed Postgres for Data Apps and Agents
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Get to know Lakebase, a fully managed Postgres for data apps and agents. Take the product tour to see how Lakebase delivers a transactional database integrated into the Lakehouse and a fully managed, serverless Postgres experience: https://
databricks.com/resources/demo
s/tours/appdev/databricks-lakebase?itm_data=demo_center?utm_source=twitter&utm_medium=organic-social
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AI Agents Infrastructure: Technical Reality and Privacy Security Risks
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NEW w/#UdbhavTiwari Mapping the technical reality & privacy/security perils of pushing AI agents into our infra We offer palliatives, but the core issues are paradigmatic: 'agency' relies on pervasive data access + ability to act w/o explicit consent.
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Generative AI Tech Stack: Six Layers Powering Autonomous Agents
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The Generative AI ecosystem is evolving into a full tech stack — powering autonomous AI agents.
From infrastructure and LLMs to RAG pipelines, agent behaviors and orchestration layers, this framework shows the 6 layers driving next-gen AI systems. Credit: @goyalshalini #AI -
Female Optimus Release Date Announcement Expected
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@grok When is the female Optimus scheduled to be released?
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ChatLLM: Intelligent Model Routing for Seamless Workflows
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We built ChatLLM by Abacus AI so you don’t have to think about models.
— Abacus.AI (@abacusai) 7 janvier 2026
You just type what you want to do.
It routes to the model that fits – reasoning, speed, creativity, longer workflows.
Keeps your flow going.
What would you use this for? pic.twitter.com/WjkBe7LBUtWe built ChatLLM by Abacus AI so you don’t have to think about models. You just type what you want to do.
It routes to the model that fits – reasoning, speed, creativity, longer workflows. Keeps your flow going. What would you use this for?