The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
CSV Cleaner & Validator — Verified Windows EXE ++++ Built a standalone Windows CSV cleaning and validation tool in Python. It detects duplicate IDs, validates required fields, email addresses and numeric amounts, preserves Unicode/Turkish characters, and separates valid and invalid records into cleaned.csv and exceptions.csv with a machine-readable summary. The packaged Windows application was verified through native GUI automation with 12/12 checks passed and 0 failures.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
When I build invoice reconciliation, I start with matches I can explain.
For CrossCheck, I connected supplier invoices, customer invoices, WHMCS records and domain events in one flow. Exact matching runs before fuzzy and context-aware matching. Uncertain items keep their source records and matching context for finance review.
I judge the workflow by how easily finance can trace an exception back to the evidence and resolve it.
https://kapilchauhan.netlify.app/case/openprovider.html
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
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 🔥