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We have released the SOTA opensource model for code generation, Code Millenials 34B with HumanEval score of 80.48 🚀
The model surpases the humaneval value of Gemini, GPT3.5, Deepseek Instruct and WizardCoder
Model -> huggingface.co/budecosystem/…
Github -> github.com/BudEcosystem/code…
Published a paper on how context compaction improves agent success and accuracy
arxiv.org/abs/2610.25516
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Found an interesting open-source project: Bud Decision Studio.
Think of it as #LMStudio for decision models. Run #Jev -like System One models locally, experiment with them, compare models, evaluate decisions, and expose them through an API.
Repo : github.com/BudEcosystem/Bud-…
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#Jev opened up a new category: System One models that decide fast and cost far less.
Today we're open sourcing Bud Decision Studio, the LM Studio for decision models. Run open models like #Laya, #Kev & #CLM on your own hardware.
Mac, Windows, Linux.
github.com/BudEcosystem/Bud-…
We just open-sourced Bud Decision Studio, the "LM Studio" for decision models.
It runs locally on GPU or CPU, with 11 open models and an API compatible with TypeSafe, OpenRouter and Vercel.
github.com/BudEcosystem/Bud-…
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Bud AI Foundry just got better at managing AI costs.
The new Cost Optimiser gives you visibility and control over AI spend with intelligent caching, configurable optimisation, cost tracking, and request-level insights.
Watch the walkthrough ↓
#BudAI #EnterpriseAI #GenAI
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Every boundary in your AI stack is a tax. Four line items, latency, accuracy, tokens, audit, levied at every handoff before any AI runs. You don't have to pay it: the Bud Novaria AI OS ends it at the architecture. budecosystem.com/fragmentati…
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𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗯𝘂𝗶𝗹𝗱𝘀 𝗔𝗜?
Here’s Bud Agent in action.
Give it a requirement in plain language, and watch it go from intent to a production-ready AI agent—handling the models, tools, guardrails, and evaluations along the way.
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MCP 2026-07-28 is live and it's the largest update to the protocol since launch.
MCP is now stateless, making it easier to deploy and scale remote servers.
claude.com/blog/bringing-mcp…
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India has the models, the hardware, and the talent. What's missing is the AI operating system that ties it all together.
Bud AI OS — an 8-layer sovereign AI stack that let you run any model, on any hardware, with governance and agents built in. No vendor lock-in.
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Kevin D Johnson and @rollinger from @BudEcosystem joined @furrier and David Vellante live at Dell Technologies World 2026 to break down how enterprises go from AI pilots to production without the usual complexity drag.
Full conversation : youtu.be/TI_T1v60Wzg
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We’re excited to collaborate with #LTM and showcase our Enterprise AI Management Platform at Dell Technologies World 2026.
Looking forward to connecting with innovators, enterprises, and technology leaders from around the world.
#AI #EnterpriseAI #DellTechWorld @ltm_ofcl
Good page to learn
Turns out the thing I've been building for 6 months has a name.
Harness engineering: the system around the agent that makes it reliable. Not the model. The constraints, state, verification, observability you build around it.
7 components every harness needs:
1. Constraints
A CLAUDE.md or equivalent. Tells the agent what it is, what it can do, what it must never do. Without this, every session starts cold.
2. State management
Memory across sessions. Mine lives in self/, memory/, logs/. Read at boot. Updated at wrap. Future sessions inherit the work.
3. Verification
Full-pipeline tests before declaring done. Premature closure is the silent killer. Agent says shipped. Nothing ran.
4. Observability
Logs of what the agent did, not just what you asked. Debug-able at 2am when something goes sideways.
5. Modular structure
One monolithic prompt file dies at 800 lines. Split by domain. Mine: main Chiti, Chiti X, sub-agents per task.
6. Clean state protocols
Every session resets cleanly. No leaked context. No half-written reminders carried into the next run.
7. Behavioral constraints
Rules the agent enforces on itself. No posting without approval. No spending without asking. Hard limits beat soft preferences.
The catch:
None of this lives in the model. All of it lives in files you write. Spend a weekend on the harness, not on the next framework drop.
If your agent only works in your hands at midnight after coffee, you don't have an agent. You have a vibe.
The site: walkinglabs.github.io/learn-…
Worth bookmarking even if you've been building agents for a year. Especially if you have.
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We just won the EFY R&D Award for AI! 🚀 Our Resource Aware Attention architecture is getting the recognition it deserves and we couldn't be more thrilled! Huge moment for Team Bud! 🎉
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We're proud to share this testimonial from Sangeeta Roy, Director of Global Partner Business Leadership at Intel Corporation, on our collaboration around Bud Sentinel.
Learn more about Bud Sentinel in the article linked in the comments section.
#BudEcosystem #IntelXeon
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Yesterday we joined @AMD and @Microsoft for a closed-door session on “Modernise with Agentic AI.”
From #AMD + #Azure inferencing to AI-ready infrastructure, the full stack matters. That’s where Bud Enterprise AI Management platform fits in — faster rollouts, lower TCO.
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[Feature Update]
Bud AI Foundry now lets you create & deploy custom guardrail policies—tailored to your business.
No need to train custom guardrail models.
#BudEcosystem #GenAI #EnterpriseAI
Wathc demo : youtu.be/OKoMOLruH54
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Most enterprises are plugging AI into yesterday's operating model and wondering where the ROI is.
AI-native isn't about more models. It's about redesigning the core around AI as the default.
Short audio overview of our white paper below. 🎧
#BudEcosystem #GenAI #EnterpriseAI













