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Academic editing and proofreading for theses, dissertations, research papers, journal manuscripts and technical reports. Grammar, clarity, structure, formatting, citations, figures, tables and technical consistency, while preserving the author's original research and meaning.
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Built a LangChain-based agentic application that turns a learning goal into a structured, personalised learning plan.
The application supports two execution modes:
⚡ ReAct Agent Mode
A tool-using agent dynamically selects custom tools for course lookup, completion-time calculation, and learning-plan generation.
🔍 Explain Mode — LCEL
A deterministic LCEL pipeline executes the planning workflow step-by-step, making the process more transparent, predictable, and easier to debug.
The project also uses Pydantic structured outputs, Groq, Python, Streamlit, and custom LangChain tools, with deterministic logic kept outside the LLM wherever possible.
This project was built to explore the practical engineering side of AI agents — tool calling, agent orchestration, structured outputs, deterministic workflows, and explainability.
The project is not currently deployed, but the complete source code and implementation are available on GitHub.
Tech: Python · LangChain · ReAct · LCEL · Groq · Pydantic · Streamlit
turning a learning goal into an actual plan is such a useful agent use case 🔥
In 90 days, AI stopped being a tool I use and became a teammate I manage.
Here's what actually changed, from where I sit as a software engineer:
→ Agents went mainstream. JetBrains found 90% of professional developers use coding agents weekly and 68% daily. Claude Code usage doubled since January (18% → 39%). Codex went 3% → 16%.
→ Models learned to drive computers. GPT-6 Astra (Sep 3) was pitched on computer use and software engineering, and shipped with gated access over its cyber capabilities.
→ Output limits blew open. Gemini 4 Argon (Sep 30) can write up to 1M tokens in one response, up from 64K. Also gated at launch.
→ Agents did real, checkable work. Dozens of Claude agents formalized Fermat's Last Theorem in Lean in 11 days: 13M lines, machine-verified. (Formalization, not a new proof. The verification is the point.)
→ The plumbing grew up. MCP's July spec went stateless, so agent tooling now deploys like ordinary web infrastructure.
→ Open weights kept pace. Alibaba released Qwen3.8-Max as a 2.4T-parameter open-weights model.
My take: code generation is getting cheap. Judgment isn't.
Anthropic's agentic coding report says engineers use AI in roughly 60% of their work but can fully delegate only 0–20% of tasks. The bottleneck has moved to architecture, review, testing, security, and keeping things alive in production.
That's where I spend my time: shipping AI features (agents, RAG, LLM integrations) into real products, with the guardrails that make them survive contact with users.
What's one task you'd hand to an agent tomorrow, and one you never would?
#AI #SoftwareEngineering #AIAgents #LLM #FullStack
managing agents instead of just using them is exactly the shift I've felt too ngl
Hi guys
I’ve been building an AI assistant that works with your own documents.
You upload files, ask a question, and the system searches the documents before giving an answer.
It can also show where the answer came from, which makes it much more useful for manuals, reports, company documents, and internal knowledge bases.
Simple idea, but very practical.
The source citation turns each answer into a traceable reference, which is huge for manuals and internal knowledge bases.