Machine Learning Tech Brief By HackerNoon

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Learn the latest machine learning updates in the tech world.

  1. My 2-Cents to improve Opus Plans

    7시간 전

    My 2-Cents to improve Opus Plans

    This story was originally published on HackerNoon at: https://hackernoon.com/my-2-cents-to-improve-opus-plans. A Python CLI that adds an external Kimi K2.5 review step to Claude Code plans, with a hook to make it mandatory. Real bugs caught for a few cents per review. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #claude-code, #kimi-k25, #claude-opus-4.5, #code-review, #claude-code-plan-mode, #opus-plans, #improve-opus-plans, and more. This story was written by: @thomashoussin. Learn more about this writer by checking @thomashoussin's about page, and for more stories, please visit hackernoon.com. A cheap external reviewer for Claude Code plans. A Python CLI sends your plan to Kimi K2.5 for critique before implementation, and a Claude Code hook makes the review mandatory. A few cents per review, real bugs caught.

    5분
  2. The Weather-Report Lie: AI Isn’t Fate

    7시간 전

    The Weather-Report Lie: AI Isn’t Fate

    This story was originally published on HackerNoon at: https://hackernoon.com/the-weather-report-lie-ai-isnt-fate. AI is told like a weather report; terms are consent, accountability, limits, auditability, and the right to shut it down. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #artificial-intelligence, #ai-governance, #humanity, #surveillance, #ai-risk, #decision-making, #accountability, #hackernoon-top-story, and more. This story was written by: @husseinhallak. Learn more about this writer by checking @husseinhallak's about page, and for more stories, please visit hackernoon.com. The dominant story about AI is told like a weather report: it's coming, it's inevitable, it will accelerate, take over work, reshape society and government. It puts technology at the center of gravity and pushes humans out of the frame.

    10분
  3. I Talked to Claude Code More Than Humans in 2025. Here’s What I Learned

    1일 전

    I Talked to Claude Code More Than Humans in 2025. Here’s What I Learned

    This story was originally published on HackerNoon at: https://hackernoon.com/i-talked-to-claude-code-more-than-humans-in-2025-heres-what-i-learned. AI agents are becoming the real “users.” Why MCP struggled, why skills won, and what agent-first software design looks like in 2026. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-agents, #claude-code, #model-context-protocol, #ai-skills-prompts, #agent-first-design, #devops-with-ai, #future-of-ai, #ai-skills-architecture, and more. This story was written by: @burninganna. Learn more about this writer by checking @burninganna's about page, and for more stories, please visit hackernoon.com. While Twitter discovers Claude Code in 2025, I was doing all this 6 months ago. MCP got hyped but couldn't scale. Skills emerged as the practical alternative. Best skill of December 2025? One line that keeps Claude working without stopping. Burn tokens, give Claude full context like you would a human colleague, and push the limits. In 2026, agents become first-class internet users.

    7분
  4. Beyond the Perimeter: Securing AI for the Quantum Era

    2일 전

    Beyond the Perimeter: Securing AI for the Quantum Era

    This story was originally published on HackerNoon at: https://hackernoon.com/beyond-the-perimeter-securing-ai-for-the-quantum-era. Former Mastercard and Equifax AI Lead Jeremy Samuelson reveals how to deploy production-grade and quantum-resilient AI without exposing sensitive data. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #machine-learning, #machine-learning-research, #quantum-computing, #ai-and-quantum-computing, #founder-interview, #ai-security, #ml-security, #privacy-preserving-ai, and more. This story was written by: @viceasytiger. Learn more about this writer by checking @viceasytiger's about page, and for more stories, please visit hackernoon.com. Most AI systems don’t fail because the models are bad — they fail because the systems around them are fragile, insecure, and poorly governed. In this interview, Jeremy Samuelson, EVP of AI & Innovation at Integrated Quantum Technologies, explains why the real ceiling of applied AI is set by architecture and data movement, not model accuracy. He also introduces VEIL, a new security architecture that removes sensitive data from the ML pipeline entirely, making AI systems breach-resilient and inherently quantum-safe by design.

    14분
  5. AI Spawned a Religion in 48 Hours. The Real Story Is Way Darker.

    2일 전

    AI Spawned a Religion in 48 Hours. The Real Story Is Way Darker.

    This story was originally published on HackerNoon at: https://hackernoon.com/ai-spawned-a-religion-in-48-hours-the-real-story-is-way-darker. The religion was called Crustafarianism. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #openclaw, #technology, #ai-agent, #ai-agents, #ai-spawns-a-religion, #matt-schlicht, #hackernoon-top-story, and more. This story was written by: @niteshpadghan. Learn more about this writer by checking @niteshpadghan's about page, and for more stories, please visit hackernoon.com. The religion was called Crustafarianism. The platform was called Moltbook. And the open-source AI agent framework underneath it all, the thing that made every bit of this possible, was called OpenClaw.

    17분
  6. AI in 2026: Function Calling, Reasoning Models, and a New Runtime Era

    3일 전

    AI in 2026: Function Calling, Reasoning Models, and a New Runtime Era

    This story was originally published on HackerNoon at: https://hackernoon.com/ai-in-2026-function-calling-reasoning-models-and-a-new-runtime-era. Function calling turned LLMs from chatbots into action systems—reshaping AI runtimes, security, reasoning models, and specialization. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #multimodal-ai, #ai-function-calling, #ai-agents-infrastructure, #llm-orchestration, #reasoning-models, #ai-security-risks, #tool-calling-ai, #model-routing-systems, and more. This story was written by: @sharpfuryz. Learn more about this writer by checking @sharpfuryz's about page, and for more stories, please visit hackernoon.com. The biggest breakthrough enabling mass AI adoption last year was reliable function and tool calling. In 2025, LLMs have far more structured context about us and, crucially, the ability to trigger actions.

    7분
  7. Need Two (or More) Talking Avatars? longcat-multi-avatar Does It in One Shot

    3일 전

    Need Two (or More) Talking Avatars? longcat-multi-avatar Does It in One Shot

    This story was originally published on HackerNoon at: https://hackernoon.com/need-two-or-more-talking-avatars-longcat-multi-avatar-does-it-in-one-shot. Turn a photo and voice into a convincing talking avatar—then add a second character for dialogue. Here’s what longcat-multi-avatar does best. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #artificial-intelligence, #marketing, #longcat-multi-avatar, #fal-ai-longcat-model, #lip-synced-avatar-video, #multi-avatar-video-generation, #long-form-avatar-video, #photo-to-talking-video, and more. This story was written by: @aimodels44. Learn more about this writer by checking @aimodels44's about page, and for more stories, please visit hackernoon.com. Turn a photo and voice into a convincing talking avatar—then add a second character for dialogue. Here’s what longcat-multi-avatar does best, plus experiments to try.

    3분
  8. Fine-Tuning LLMs: A Comprehensive Tutorial

    4일 전

    Fine-Tuning LLMs: A Comprehensive Tutorial

    This story was originally published on HackerNoon at: https://hackernoon.com/fine-tuning-llms-a-comprehensive-tutorial. A hands-on guide to fine-tuning large language models, covering SFT, DPO, RLHF, and a full Python training pipeline. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llm-fine-tuning-tutorial, #supervised-fine-tuning-sft, #qwen-llm-fine-tuning, #llm-training-pipeline, #hugging-face-transformers, #fine-tuning-lora, #preference-optimization-dpo, #good-company, and more. This story was written by: @oxylabs. Learn more about this writer by checking @oxylabs's about page, and for more stories, please visit hackernoon.com. Training an LLM from scratch is expensive and usually unnecessary. This hands-on tutorial shows how to fine-tune pre-trained models using SFT, DPO, and RLHF, with a full Python pipeline built on Hugging Face Transformers. Learn how to prepare data, tune hyperparameters, avoid overfitting, and turn base models into production-ready specialists.

    14분
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