Ditto

135 posts

Ditto

Ditto

@dittops92

Kerala, India 参加日 Haziran 2023
80 フォロー中8 フォロワー
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Ditto
Ditto@dittops92·
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/c… Github -> github.com/BudEcosystem/c…
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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
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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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
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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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
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
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Ditto@dittops92·
Good page to learn
Ramya Chinnadurai 🚀@code_rams

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-harness-… Worth bookmarking even if you've been building agents for a year. Especially if you have.

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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
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! 🎉
Bud Ecosystem tweet mediaBud Ecosystem tweet media
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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
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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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
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.
Bud Ecosystem tweet media
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Ditto@dittops92·
@intuitiveml Evaluating final output alone is not correct. The path is important for long term accuracy. Eh in code if path is not correct agents create unwanted code and gradually degrade the agnet accuracy
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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
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
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Ganesh Ghatti
Ganesh Ghatti@GaneshGhatti6·
Laptop CPU beats $15,000 A100 GPU at running AI guardrails. Here's how Bud Ecosystem did it.
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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
Grateful to be featured in @the_hindu ! The biggest breakthroughs don't always come from more resources. Sometimes they come from questioning why things are built the way they are. We questioned the norms of AI. We saw its problems and invented the solutions.
Bud Ecosystem tweet media
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dominik kundel
dominik kundel@dkundel·
I built a new plugin! You can now trigger Codex from Claude Code! Use the Codex plugin for Claude Code to delegate tasks to Codex or have Codex review your changes using your ChatGPT subscription. Start by installing the plugin: github.com/openai/codex-p…
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Ditto
Ditto@dittops92·
Claiming my ClawBench agent ag_d4b811fe68b841d689cb53f9 with verification code cb-10c75498 #ClawBench
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Daniel Hnyk
Daniel Hnyk@hnykda·
LiteLLM HAS BEEN COMPROMISED, DO NOT UPDATE. We just discovered that LiteLLM pypi release 1.82.8. It has been compromised, it contains litellm_init.pth with base64 encoded instructions to send all the credentials it can find to remote server + self-replicate. link below
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Ditto
Ditto@dittops92·
@code_rams Both are not looking at different problem. Claude support most of the OpenClaw features. Recently they are competing each other. Claude is launching features daily to make OpenClaw redundant.
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Bud Ecosystem
Bud Ecosystem@BudEcosystem·
#GenAI is still locked behind $15K GPU servers. We think that's an architecture problem, not a hardware one. Introducing Bud Sentinel, a guardrail system that runs on a laptop and outperforms SOTA guardrail models on an A100. #Guardrails #LLMs
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