Looking at your GeForce RTX 2050 laptop GPU, here's what I recommend:
The team needs an internal workflow that ingests messy legal-style documents, pulls usable information out of them, and turns that information into grounded draft outputs an operator can edit.
The inputs will not be clean. Expect scanned pages, low-resolution PDFs, handwritten notes, partially illegible records, and inconsistently formatted files. Your system has to cope with that.
At a high level, the system you build should:
I want to add a logging middleware to my FastAPI app. Log every request and
response with: method, path, status, latency, a correlation ID, and redact
auth headers. Structured JSON output. Before you write any code, ask codex
for a second opinion on the design — specifically: async vs sync middleware,
how to propagate the correlation ID through downstream async tasks, and
what the cleanest redaction point is. Then reconcile and implement.
User question:
{truncated user question}
Candidates:
Candidate 1:
Tag: {tag}
Matched question: {truncated question}
Answer: {truncated answer}
Retrieval score: {score:.6f}
Session: b5538e14 | Date: 2026-04-01 05:59:40 | Branch: HEAD | Turns: 25 Project: /home/synesis/bada
Developer asked for a single page application. Claude chose React + Vite over Next.js (lighter for a true SPA), scaffolded via create-vite, then rewrote the default template into a dark-themed portfolio with nav, hero, projects grid, about, and contact sections. Build succeeded in 91ms with no errors.
| #!/usr/bin/env python3 | |
| """FastAPI batch ASR server — Bengali Whisper (faster-whisper / CTranslate2). | |
| Usage: | |
| python serve.py | |
| python serve.py --port 8001 --host 0.0.0.0 | |
| """ | |
| import base64 | |
| import io |
This repository is a Python CLI tool called ve for documentation-driven development. It turns the prompts you'd normally give an AI agent into persistent, discoverable documentation — creating a self-building institutional memory.
ve command is defined in pyproject.toml (ve = "ve:cli").src/ve.py.src/cli/__init__.py (Click framework).Current baseline was the temporal PARSeq-Small/parseq setup (e.g., embed_dim=384, enc_depth=12, ~23.8M-class model family).
Two approaches are proposed:
0-9) plus control token(s), unlike broader OCR character sets.0-9 + EOS), not 100 jersey classes. The model learned digit identities and sequence order, so unseen combinations were compositional.A production-ready Bengali conversational AI system for National Identity Card (NID) and voter registration queries. The system uses semantic search (RAG) with multi-turn form conversations, interruption handling, and state management.
Core Challenge: Build an intelligent chatbot that understands Bengali queries about NID/voter registration, retrieves relevant answers from a knowledge base, and handles complex multi-turn conversations like form filling.
The Bangladesh Election Commission NID chatbot is a conversational AI system that helps citizens with National Identity Card (NID) and voter registration queries. The system operates in two distinct modes: