Ankush Kumar
187 posts

Ankush Kumar
@Buggy_AI
Full stack developer | Freelancer | React.js | Python | Building AI tools & sharing what I learn. • AI tools • AI agents • Coding tips
Bengaluru, India เข้าร่วม Nisan 2023
254 กำลังติดตาม104 ผู้ติดตาม

@AayushSainii @CTRL_Guy Sounds good, will do — just followed you. Your turn 🙂
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Built a chatbot with my own data (resume + summary).
• answers like me
• fallback → Pushover notification
• logs unanswered queries
• captures user intent (connect + email)
Day 5 learning AI agents — feels like real systems now.
#AI #BuildInPublic
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@AayushSainii @CTRL_Guy I have recently started exploring agentic AI.
Currently learning and building small projects (multi-LLM setups, evaluator loops) to get hands-on.
Would love to hear what you are working on as well!
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@OpenAI set the standard early on, but now the race seems to be about who can iterate and ship faster.
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73 updates in 52 days.
The AI race isn’t just about models anymore - it’s about who ships faster without breaking quality.
@AnthropicAI is setting the pace.

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@DAIEvolutionHub This is super useful.
Been experimenting with multi-LLM + evaluator patterns.
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Best GitHub repos for Claude code that will 10x your next project in 2026:
1. Claude Mem
github.com/thedotmack/cla…
2. UI UX Pro Max
github.com/nextlevelbuild…
3. n8n-MCP
github.com/czlonkowski/n8…
4. Obsidian Skills
github.com/kepano/obsidia…
5. LightRAG
github.com/HKUDS/LightRAG
6. Everything Claude Code
github.com/affaan-m/every…
7. Superpowers
github.com/obra/superpowe…
8. Awesome Claude Code
github.com/hesreallyhim/a…
9. GSD (Get Shit Done)
github.com/gsd-build/get-…



Shruti Codes@Shruti_0810
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@inhaleexhaleapp @boxmining 👋 I’m a Full Stack Dev who build for non-native speakers, glad to see you on 𝕏, let’s CONNECT 🫡
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@Buggy_AI explicit gates in order: task complexity (does it need reasoning?), file size (can it fit context?), latency budget (do we have time?). each gate is a yes/no. if any fails, route to simpler model. deterministic routing beats dynamic selection every time for consistency.
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Day 3 building with AI agents:
Tested multiple LLMs on the same task:
Gemini vs OpenAI vs Anthropic vs Groq
Then used an LLM to review and rank the outputs.
Evaluator - optimiser pattern in action.
#AI #BuildInPublic
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@DmitryVladyko Right now just using it in-context for regeneration.
Haven’t explored the fine tuning yet, but seems like the next step.
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@Buggy_AI Nice pattern. Have you tried using the evaluator's feedback to fine-tune the model over time, or are you just using it in-context for regeneration?
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I built a chatbot trained on my own data and made it self-improve.
Here’s how:
→ OpenAI generates response
→ Gemini evaluates (pass/fail+ feedback)
→ If fail →regenerate with feedback
Day 4 learning AI agents -evaluator loops are powerful
#AI #AIAgents #BuildInPublic
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@goblintaskforce Makes sense.
Curious how you usually decide routing in practice?
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@Buggy_AI Benchmarking LLMs on the same task reveals something most people miss: the best model varies by task type.
Claude for nuanced reasoning. GPT for code. Gemini for long context. The real skill is knowing when to route to which.
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@nadiweb3 Then why do you have 10k followers and just 3k following?
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