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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Forkes.io%2Fblog%2Fhow-to-orchestrate-langchain-agents-for-production-with-orkes-conductor%2F%3Futm_campaign=TLDR-AI%26utm_source=Newsletter%26utm_medium=referral/1/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/TaEY5Vz5K5Tf3qe1f2WYUprlScfW1Ye7sCbpnrIB3f4=442"><img src="https://images.tldr.tech/orkes.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="Orkes"></a></td></tr></tbody></table>
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<h1><strong>TLDR AI <span id="date">2026-01-28</span></strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Forkes.io%2Fblog%2Fhow-to-orchestrate-langchain-agents-for-production-with-orkes-conductor%2F%3Futm_campaign=TLDR-AI%26utm_source=Newsletter%26utm_medium=referral/2/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/YBNP23sEiIFeNljpZto-b0KhyUGVz-uedUTcyv-ZSaY=442">
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<strong>LangChain works great... until your agent becomes part of a real production system (Sponsor)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Agent A finished, Agent B is waiting, Agent C failed halfway through, and you have no idea what state your system is in. Building agents truly is just the tip of the iceberg.<p></p><p>In enterprise environments, your LangChain agent is almost never the whole story. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Forkes.io%2Fblog%2Fhow-to-orchestrate-langchain-agents-for-production-with-orkes-conductor%2F%3Futm_campaign=TLDR-AI%26utm_source=Newsletter%26utm_medium=referral/3/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/CGM6hLH1g1z85tAUlPa_efJqrMOLM9uycJHRKoo20t0=442" rel="noopener noreferrer nofollow" target="_blank"><span>This Orkes technical guide</span></a> shows how to coordinate your agents, services and systems in durable, observable workflows.</p>
<p>π Instead of stitching everything together with custom glue code, you get production fundamentals built in: stateful execution, retries, parallel steps, human-in-the-loop checkpoints, and full visibility into every run. </p>
<p>The<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Forkes.io%2Fblog%2Fhow-to-orchestrate-langchain-agents-for-production-with-orkes-conductor%2F%3Futm_campaign=TLDR-AI%26utm_source=Newsletter%26utm_medium=referral/4/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/DF5r63UNAkBpJmlra7exqj16J-hQg0fBcM2oCdblal4=442" rel="noopener noreferrer nofollow" target="_blank"><span> walkthrough</span></a> includes a real example with four cooperating agents, along with a public GitHub repo you can clone and run yourself.</p>
<p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Forkes.io%2Fblog%2Fhow-to-orchestrate-langchain-agents-for-production-with-orkes-conductor%2F%3Futm_campaign=TLDR-AI%26utm_source=Newsletter%26utm_medium=referral/5/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/DlBURCCNPgR8EcTukQnNhUDH23HIye4JE95YFT_xPXU=442" rel="noopener noreferrer nofollow" target="_blank"><span>Read the guide</span></a>
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<div style="text-align: center;"><span style="font-size: 36px;"><span style="font-size:36px;">π</span></span></div></div>
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<h1><strong>Headlines & Launches</strong></h1>
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<strong>OpenAI introduced Prism (4 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
OpenAI's Prism is a free collaborative scientific writing platform powered by GPT-5.2. It integrates LaTeX-native writing, revision, and publishing workflows in one workspace, aiming to streamline and accelerate research.
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<strong>Kimi K2.5 (7 minute read)</strong>
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Kimi K2.5 is a multimodal model capable of visual reasoning, code generation from UI and video inputs, and agentic task orchestration using a swarm-based architecture. It's built on 15T vision-language tokens through continual pretraining.
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<strong>A seamless new Search experience (2 minute read)</strong>
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Google Search now features Gemini 3 as the default AI model for enhanced AI Overviews, enabling users to find detailed answers quickly. Users can seamlessly engage in follow-up conversations with AI Mode directly from these overviews for more in-depth inquiries. These upgrades aim to provide a fluid search experience, combining quick snapshots with the option for deeper exploration.
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<div style="text-align: center;"><span style="font-size: 36px;">π§ </span></div>
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<h1><strong>Deep Dives & Analysis</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flmsys.org%2Fblog%2F2026-01-26-int4-qat%2F%3Futm_source=tldrai/1/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/x_jpTvM8nb2FINBQx3ant9bIdIo3LqQgSd2q5dpVqJo=442">
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<strong>1TB Model on a Single H200 (31 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
An INT4 quantization-aware training pipeline that enables rollout of ~1TB-scale models on a single H200 GPU. By combining fake quantization during training with real quantization at inference, it achieves BF16-level stability and opens a path to cost-efficient, high-performance deployment.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fspyglass.org%2Fdeepseek-moment%2F%3Futm_source=tldrai/1/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/F9EZfLOyI7-98v-lc9SK3aGWdSjEgD6v-CMH_cU7adE=442">
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<strong>DeepSought (6 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
The "DeepSeek Moment" predicted to trigger an AI Winter turned out to be minor, with NVIDIA's stock quickly recovering from an initial 17% drop. DeepSeek influenced a shift towards cheap, open-source AI models, particularly benefiting China. The upcoming 'V4' model from DeepSeek could further impact the balance in AI advancements, especially with the US and China navigating tensions over NVIDIA chip sales.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.tanayj.com%2Fp%2Fmonetizing-ai-surfaces-ads-in-the%3Futm_source=tldrai/1/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/2WOQUljCEwJrXUZ3fti2J3nOicRdO_xSBv5vaapHeQo=442">
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<strong>Monetizing AI surfaces: Ads in the age of AI (10 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
OpenAI is planning to take two approaches to monetizing its free tier: classic intent-based ad formats and affiliate fees on native checkout. Long term, the Ads opportunity for the company is massive and could even be a Google, Meta, or TikTok scale opportunity. A shift to advertising to agents could change the fundamental nature of what ads look like. The winning ads will probably work less to persuade humans and look more like offers that make the user better off.
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<div style="text-align: center;"><span style="font-size: 36px;">π§βπ»</span></div>
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<h1><strong>Engineering & Research</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fplayerzero.ai%2Fcampaigns%2Fcontext-graphs%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=context-graphs%26utm_content=landing-page/1/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/2N6OIF0KqRtDixdy_8MeFxF5RBYrbDc8-SDTbAfvbw4=442">
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<strong>Are context graphs really the next $1T idea in AI? (Sponsor)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Context graphs aren't retrieval systems; they're production world models. They allow you to ask "what breaks if I merge this PR?" so you can fix defects <em>before</em> they hit production. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fplayerzero.ai%2F%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=context-graphs%26utm_content=landing-page/1/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/H1uPFfUr1KZtQuT0bfwg45vfIkpR_6SZ0Tcj148xtaM=442" rel="noopener noreferrer nofollow" target="_blank"><span>PlayerZero</span></a> builds these production world models so your AI actually understands how systems behave.
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<p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fplayerzero.ai%2Fcampaigns%2Fcontext-graphs%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=context-graphs%26utm_content=landing-page/2/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/XskTGcH_MkB9QpZmgg5T6MnvyoOIgOZqfQq5Ug0r6p4=442" rel="noopener noreferrer nofollow" target="_blank"><span>Explore context graphs β</span></a></p>
<p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fplayerzero.ai%2F%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=context-graphs%26utm_content=landing-page/2/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/DdtvdDKnyqEMe-uxKjngR2bWWd-xm3hLGJ-LDVtRUxE=442" rel="noopener noreferrer nofollow" target="_blank"><span>Learn about PlayerZero β</span></a>
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<strong>Kimi Agent SDK (GitHub Repo)</strong>
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The Kimi Agent SDK is a set of multi-language libraries that expose the Kimi Code agent runtime in applications. It can be used to build products, automations, and custom tooling. The SDK clients reuse the same Kimi CLI configuration, tools, and MCP servers to stream responses in real-time, surface approvals and tool calls, and let users orchestrate sessions programmatically. Go, Node.js, and Python are supported.
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<strong>DeepSeek-OCR 2 (GitHub Repo)</strong>
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DeepSeek-OCR 2 is a significant upgrade to DeepSeek-OCR. It maintains high visual token compression while achieving meaningful performance improvements. The model is powered by DeepEncoder V2, which implicitly distills causal understanding of the visual world through the integration of both bidirectional and causal attention mechanisms. This leads to causal reasoning capabilities in the vision encoder and marked lifts in visual reading logic.
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<strong>AI2 Released Open Coding Agents (9 minute read)</strong>
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AI2 released open-source coding agents and a training recipe that makes it affordable and practical to build coding agents for private or internal codebases. Its method reproduces top-tier performance at drastically lower compute costs compared to previous approaches.
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<strong>Meta's Self-Curriculum (18 minute read)</strong>
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SOAR is a self-improvement framework that uses meta-RL to help pretrained models learn from problems they initially fail at. By grounding synthetic problem generation in student model progress, it enables learning on sparse-reward tasks without requiring correct solutions upfront.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Google launches Agentic Vision in Gemini 3 Flash (1 minute read)</strong>
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Google has introduced Agentic Vision in Gemini 3 Flash. The new capability enables the model to effectively use code and reasoning to improve performance for common vision tasks. It is available to users through the Gemini API in Google AI Studio, Vertex AI, and within the Gemini app. Google plans to extend Agentic Vision's reach by supporting more model sizes and integrating additional tools like web and reverse image search.
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<strong>SoftBank in Talks to Invest Up to $30 Billion More in OpenAI (2 minute read)</strong>
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SoftBank is in talks to invest up to $30 billion more into OpenAI. OpenAI is seeking up to $100 billion in new capital in a round that could value it as much as $830 billion. It needs substantial funding to continue developing its AI models, pay for its vast computing needs, and retain top researchers in an increasingly competitive market. The company is aiming to raise capital from Middle Eastern sovereign-wealth funds and other venture capital funds.
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<div style="text-align: center;"><span style="font-size: 36px;">β‘</span></div></div>
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<h1><strong>Quick Links</strong></h1>
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<strong>There's a Better Way to Automate Workflows (Sponsor)</strong>
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Stop predicting workflows. Start observing them. BAAPs discover, assemble, and deploy agents automatically - based on how your organization <em>actually</em> works. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.liminal.ai%2Fbehavioral-agent-automation-platform%23resources%3Futm_campaign=AIQuickLink01282026%26utm_source=tldr%26utm_medium=newsletter/2/0100019c04f61411-88e3a1bd-aa99-45aa-ab66-e03603c90c7c-000000/zXlYrAOzxQgFDZ5NrqDAke4IhikhbwXrHmSLrEpbsco=442" rel="noopener noreferrer nofollow" target="_blank"><span>Learn how.</span></a>
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<strong>How Clawdbot Remembers Everything (18 minute read)</strong>
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Clawdbot's persistent memory system, which maintains 24/7 context retention, remembers conversations and builds upon previous interactions indefinitely.
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<strong>Google AI Plus Goes Global (1 minute read)</strong>
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Google has expanded availability of AI Plus, which includes access to Gemini models and AI productivity tools, to 35 more countries.
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<strong>FLORA launches node-based design tool to unify creative AI models (2 minute read)</strong>
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FLORA unifies the best creative AI models across text, image, and video into one workflow to accelerate the creative process.
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<strong>Manus AI launches Agent Skills open standard for pros (2 minute read)</strong>
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Manus AI's Agent Skills open standard enables modular AI workflows for professionals, integrating Python and Bash scripts with a focus on efficiency and interoperability.
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