Helping Startups & Enterprises Turn AI into Revenue-Ready, Scala

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The Future of AI Coding Isn't Better Code—It's Better Engineering

The Real AI Coding Problem Nobody Is Talking About   Why “hallucinations” are just the symptom — and the actual disease is something I call Engineering Drift.     Six months ago, I sat in a kickoff call for a Customer Support Platform.   Nothing exotic. Tickets, chat, uploads, notifications, a billing plan, an admin dashboard. The kind of enterprise build every Solution Architect has led a dozen…

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Self-Scaffolding LLMs: How Ornith-1.0 Rewrites Its Own Harness Mid-Training

A technical breakdown of DeepReinforce’s self-improving agentic coding models   There’s a quiet assumption baked into most RL post-training pipelines for coding agents: a human designs the harness, and the model just gets better at using it. The scaffold — memory management, error handling, tool orchestration, retry logic — stays fixed. The policy is the only thing that’s allowed to…

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Tools: Spec Kit (GitHub)

Reference: https://github.com/github/spec-kit   What it is   An open-source toolkit (117k stars) that implements Spec-Driven Development (SDD) — a workflow where specifications become executable and directly generate working implementations, instead of being scaffolding you write and discard once “real” coding starts. It ships as a CLI (specify) plus a set of slash commands (/speckit.*) that work…

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Tools: Deep Agents (langchain-ai)

Reference: https://github.com/langchain-ai/deepagents   What it is An open-source, “batteries-included” agent harness built on LangGraph — an opinionated agent that runs out of the box, where every piece can be extended, overridden, or replaced. 25.1k stars, actively maintained (latest release June 2026).   Use case   Instead of building your own agent loop from scratch on raw LangGraph, you get…

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Tools: RAG-Anything (HKUDS)

Reference: https://github.com/HKUDS/RAG-Anything   What it is   An all-in-one multimodal RAG framework built on top of LightRAG (also from HKUDS) that handles text, images, tables, and mathematical equations within a single unified pipeline — instead of needing separate tools for each content type. 15.1k stars, actively maintained, with a technical report on arXiv.   Use case   For documents that…

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Tools: ade-python (LandingAI)

Reference: https://github.com/landing-ai/ade-python   What it is The official Python SDK for LandingAI’s Agentic Document Extraction (ADE) REST API — a fully-typed library with Pydantic response models, sync and async clients, and support for pluggable HTTP backends (httpx or aiohttp). Install with pip install landingai-ade   Use case This is the programmatic layer that powers what the…

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News: Microsoft Makes Governance the Gate for Enterprise AI Agents

At Build 2026, Microsoft shipped the Agent 365 SDK into general availability, baking identity, policy, and data controls directly into how agents are built — not bolted on after they misbehave in production. A new Agent Registry (Defender + Entra + Intune) surfaces unmanaged local agents and MCP servers orgs didn’t know they were running, while Defender–GitHub Advanced Security is now GA for…

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Tools: ADE Document Processing Skills (LandingAI)

Reference: https://github.com/landing-ai/ade-document-processing-skills   What it is Agent skills that teach coding assistants like Claude Code, Cursor, and Roo Code how to write Python scripts using LandingAI’s Agentic Document Extraction (ADE) — a vision-first document AI that parses complex, real-world documents into structured, auditable data without templates or ML training.   Use…

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Tools: OpenWiki (langchain-ai)

Reference – https://github.com/langchain-ai/openwiki   What it is OpenWiki is a CLI that writes and maintains documentation for your codebase, built specifically for AI coding agents.   Use case If you’re working with coding agents (Claude Code, Copilot, etc.) that need context on a codebase, OpenWiki auto-generates a structured openwiki/ docs folder from your repo and keeps it fresh. You…

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News: OpenAI and Broadcom Unveil Jalapeño - Their First Custom AI Inference Chip

OpenAI has officially announced Jalapeño, its first custom-built AI inference processor, developed in partnership with Broadcom. Unlike general-purpose GPUs, Jalapeño is purpose-built for large language model (LLM) inference, enabling faster response times, improved power efficiency, and lower operating costs for AI applications such as ChatGPT, Codex, and future AI agents.   The chip was…

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News: China's Meituan Unveils LongCat-2.0 - Trained Entirely on Domestic AI Chips

Chinese technology giant Meituan has introduced LongCat-2.0, a 1.6-trillion-parameter open-source large language model that the company claims is the first frontier-scale AI model trained and deployed entirely on domestically developed Chinese AI chips. The model was built using a 50,000-chip domestic compute cluster, marking a significant milestone in China’s drive toward AI hardware…

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Why a 7B Parameter Model Won't Run Comfortably on a 14 GB GPU (And Why Most Engineers Get This Wrong)

If you’ve recently started working with Large Language Models (LLMs), you’ve probably seen a calculation like this:   7 Billion Parameters × 2 Bytes (FP16) ≈ 14 GB   At first glance, it seems perfectly reasonable to conclude: “A GPU with 14 GB of VRAM should be enough.”   Unfortunately, that’s one of the most common misconceptions I encounter when discussing AI infrastructure with engineering…

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Introducing PersonaGraph: The Problem Every AI Power User Ignores

The Moment That Started Everything   Open ChatGPT Type: “My name is Amit. I’m building a FastAPI backend with SQLite.”   Switch to Claude. Ask: “What stack am I using?”   Claude doesn’t know. Of course it doesn’t. You never told Claude. You told ChatGPT. Those are different products, different companies, different memory silos.   Your knowledge — your name, your project, your decisions, your…

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RAG Evaluation Techniques — A Field Guide for Multi-Turn Voice Agents

RAG Evaluation Techniques — A Field Guide for Multi-Turn Voice Agents   How to rigorously evaluate retrieval-augmented generation (RAG) in conversational voice agents — distinct from simple document Q&A interfaces. Written from real-world implementation experience with production voice systems.   All examples use the fictional company FleetPulse (fleet telematics provider) to illustrate concepts…

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Why RAG Systems Sometimes Answer Questions Nobody Asked

A Production Lesson Every AI Engineer Eventually Learns   One of the most surprising moments when deploying a Retrieval-Augmented Generation (RAG) system to production is watching users become frustrated by an AI that appears highly intelligent but somehow feels socially unaware.   The user says: Thank You The AI responds: According to the my knowledge based The user says: Okay, I understand The…

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Loops: The Quiet Skill Behind Every AI System That Actually Scales

Why the Future of AI Isn’t About Better Models—It’s About Better Loops   Every week a new AI model arrives. A larger context window. A better benchmark score. A more impressive demo.   The industry conversation usually follows the same pattern: Is GPT-5 better than Claude? Is Claude better than Gemini? Is Gemini better than Qwen? Which model should we use?   But after building and deploying…

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Learn AI for Free: 10 Platforms From OpenAI, Google, Microsoft, NVIDIA & More

A few years ago, learning Artificial Intelligence felt expensive.   People spent thousands of dollars on bootcamps, certifications, and online programs hoping to gain AI skills that could improve their careers.   Today, something remarkable has happened.   The companies building the world’s most advanced AI systems are teaching people for free.   Not through marketing videos. Not through random…