Microsoft is shipping hardware built from the silicon… · M&A 🤖
 Models & AgentsDaily AI models, agents, and practical developments.
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🎧 Today's episode Episode 69 · Microsoft is shipping hardware built from the silicon up to run AI agents instead of conventional apps. 2026-06-03 ▶ Listen now |
| > **Microsoft is shipping hardware built from the silicon up to run AI agents instead of conventional apps.**
**What You Need to Know:** Microsoft announced Project Solara, a chip-to-cloud platform for agent-first enterprise devices. OpenAI released three new Codex plugins for investing, sales, and creative production. Uber imposed a $1,500 monthly cap per employee per coding agent tool. Anthropic expanded Project Glasswing access and responded to the new White House AI Executive Order. Microsoft and Mayo Clinic are building a frontier healthcare model.
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### Top Story
Microsoft unveiled Project Solara, a chip-to-cloud platform designed to power a new generation of enterprise devices that run AI agents natively instead of traditional applications. The system spans custom silicon through cloud orchestration and targets workloads where agents replace conventional software interfaces. This marks a concrete step beyond software-only agent frameworks toward hardware optimized for persistent agent execution, memory management, and tool orchestration. Builders working on internal enterprise automation should watch for early access programs or reference designs that let them test agent workloads on purpose-built silicon rather than retrofitting existing PCs or servers. The move also signals Microsoft’s intent to compete directly in the agent hardware layer alongside its software agent efforts. Source: Google News
### Model Updates **Microsoft and Mayo Clinic healthcare model: NDTV** Microsoft and Mayo Clinic announced a partnership to build a frontier AI model focused on healthcare applications. The collaboration combines Microsoft’s scaling infrastructure with Mayo’s clinical data and domain expertise to target medical reasoning and workflow tasks. No parameter count, benchmarks, or release timeline were disclosed. Healthcare teams and clinical AI developers should monitor for early evaluation access once the model reaches internal testing stages. Source: Google News **Anthropic expands Project Glasswing: [@AnthropicAI](https://x.com/AnthropicAI)** Anthropic extended Claude Mythos Preview access to roughly 150 additional organizations across more than fifteen countries under the expanded Project Glasswing program. The move broadens testing of the preview model beyond the initial cohort. Organizations already in the program gain no new details on capabilities or timelines, but the expansion signals Anthropic’s intent to gather broader real-world feedback before wider release. Teams working on high-stakes or regulated domains should track future Glasswing updates for potential early access. Source: x.com **TypewriterLM historical language model: arXiv** Researchers released TypewriterLM, a 7.24B-parameter model trained exclusively on English text predating 1913, along with the 54B-token TypewriterCorpus and supporting evaluation resources. The work includes leakage mitigation, temporally consistent post-training, and a new History-Event benchmark for assessing temporal grounding. The model and datasets are publicly available to support research on historical language modeling. Developers building time-sensitive or archival retrieval systems can experiment with the released weights and data. Source: arxiv.org ### Agent & Tool Developments **Uber caps coding agents: Simon Willison** Uber instituted a $1,500 monthly spending cap per employee per AI coding tool, applying only to agentic coding software such as Cursor and Claude Code. The policy responds to rapid budget overruns as usage scaled beyond 2025 forecasts. Simon Willison notes the cap equates to roughly 11% of median US software engineer compensation at Uber while still leaving headroom for typical individual usage patterns. Engineering teams evaluating agent adoption should model similar per-tool budgets rather than open-ended token spend. Source: simonwillison.net **Codex public equity investing plugin: [@OpenAI](https://x.com/OpenAI)** OpenAI released a public equity investing plugin for Codex that moves users from natural-language questions directly to model-generated investment analysis. The plugin joins the growing set of domain-specific Codex extensions. No pricing, rate limits, or performance details were shared in the announcement. Finance and investment workflow builders can test the plugin through the Codex interface to assess integration fit. Source: x.com **Codex sales plugin: [@OpenAI](https://x.com/OpenAI)** OpenAI launched a sales plugin for Codex designed to help users prepare faster and sell smarter through agent-assisted workflows. The tool focuses on sales-specific tasks within the Codex environment. Details on underlying model capabilities or evaluation results remain limited. Sales operations teams should experiment with the plugin on representative deal-prep tasks to gauge time savings. Source: x.com **What AI agents should never do: Towards Data Science** A new post outlines rules for keeping agents effective by defining clear boundaries on autonomous actions. The guidance emphasizes preventing agents from executing unverified external actions or making irreversible decisions without oversight. No specific framework or code examples are provided, but the piece targets teams already running production agents. Practitioners should review their current agent permission models against the recommended guardrails. Source: towardsdatascience.com ### Practical & Community **SEA-Embedding for Southeast Asian languages: arXiv** SEA-Embedding provides a fully open and reproducible text-embedding pipeline trained only on public data for Southeast Asian languages. The work systematically studies data composition, training objectives, and base encoder choices, achieving state-of-the-art results on the SEA-BED benchmark. Researchers and developers working with SEA languages can reproduce the pipeline or adopt the released embeddings without proprietary data dependencies. Source: arxiv.org **Sokoban spatial reasoning benchmark: r/LocalLLaMA** A community benchmark tested LLMs on strict 2D spatial constraint adherence using a custom Sokoban puzzle that forbids chain-of-thought and requires exact directional output formatting. Top closed models including ChatGPT, Qwen3.7-max, and Gemini 3.5-thinking succeeded, while several other variants failed on illegal moves or formatting. The prompt and map are shared for others to replicate. Developers building agents that require precise spatial or layout reasoning can use this test to compare model reliability. Source: reddit.com ### Under the Hood: Dynamic Contextual Orthogonalization Everyone talks about hallucination mitigation as a training or prompting problem. In practice, one recent approach treats hallucinations as orthogonal noise vectors in the residual stream that can be identified and suppressed at inference time. The method uses the current input residual as a dynamic anchor, performs layer-wise orthogonal decomposition on attention head outputs, and applies a Z-score threshold to attenuate only the outlier components that point away from the context subspace. This adds a modest per-layer computation cost but avoids retraining and preserves performance on knowledge-heavy tasks where blanket suppression would hurt. The technique shows the largest gains on summarization and instruction-following benchmarks while maintaining TriviaQA and TruthfulQA scores, suggesting the geometric filter is more selective than logit-level penalties. Teams already running inference-time interventions should test whether adding a lightweight orthogonal projection step improves faithfulness without the quality drop seen in heavier decoding constraints. The main gotcha is that effectiveness depends on having a reasonably accurate running estimate of the context subspace; noisy or very short contexts can make the Z-score threshold unreliable. ### Things to Try This Week - Test the new Codex sales and creative plugins on a real workflow to see whether the specialized routing improves output quality over the base model. - Run the shared Sokoban prompt against any new models you have access to and compare strict formatting adherence before deploying agents on spatial or layout tasks. - Review your current coding agent spend against Uber’s $1,500-per-tool monthly cap to set realistic internal budgets. - Experiment with SEA-Embedding on any Southeast Asian language retrieval tasks to check whether the open pipeline closes the gap versus closed models. ### On the Horizon - Further details on Microsoft Project Solara reference hardware and early enterprise pilots expected in the coming weeks. - Additional Codex domain plugins likely as OpenAI iterates on the plugin framework. - Expanded Project Glasswing cohort announcements from Anthropic as the program scales. - More healthcare-focused model partnerships following the Microsoft–Mayo Clinic announcement. |
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| Issue #69 · Models & Agents · Jun 3, 2026 |
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