Best of Machine Learning — September 2025

  1. 1
    Article
    Avatar of alpfx74nvso3ceoulnvgsJosh M.·31w

    GPT-5 is Trash.

    ChatGPT-5 has received significant criticism from users who report that responses are shorter, blander, and less engaging than previous versions. Despite being marketed as PhD-level intelligence, the model still makes basic errors in math and reasoning while suffering from hallucinations. OpenAI's removal of model selection options and implementation of an autoswitcher has frustrated users, leading many to believe this was a cost-saving measure rather than genuine improvement. The backlash was severe enough that OpenAI restored access to older models like GPT-4o.

  2. 2
    Article
    Avatar of swirlaiSwirlAI·32w

    Learning AI Engineering in 2025

    An AI engineering bootcamp instructor reflects on the success of their first cohort, sharing metrics like 40 hours of live lectures and 250 pages of materials. The program focuses on building production-ready AI systems end-to-end, with upcoming improvements including deeper evaluation focus, context engineering, guest lectures, and Modal cloud partnerships. The bootcamp targets data scientists, ML engineers, founders, and software engineers looking to transition into AI engineering.

  3. 3
    Article
    Avatar of hnHacker News·32w

    MIT Study Finds Artificial Intelligence Use Reprograms the Brain, Leading to Cognitive Decline

    MIT researchers conducted a study using EEG brain scans to examine how ChatGPT usage affects cognitive function during essay writing tasks. The study found that students who relied on AI assistants showed weakened neural connectivity, impaired memory recall, and reduced sense of ownership over their work. Participants using ChatGPT were unable to quote from essays they had just written, while those using traditional methods or search engines maintained better cognitive engagement. The research suggests that AI dependency leads to cognitive offloading, where the brain adapts to rely on external tools at the expense of critical thinking and memory formation.

  4. 4
    Article
    Avatar of freecodecampfreeCodeCamp·32w

    How to Fine-Tune Large Language Models

    A comprehensive course covering fine-tuning techniques for large language models, including supervised fine-tuning, reinforcement learning with human feedback (RLHF), and QLoRA methodology. The course explains the differences between fine-tuning, pre-training, and prompt engineering, with practical applications and case studies for specializing LLMs for specific domains.

  5. 5
    Article
    Avatar of hnHacker News·31w

    TikTok Won. Now Everything Is 60 Seconds.

    TikTok has fundamentally transformed digital culture by industrializing human attention through sophisticated algorithmic optimization. The platform's instant learning from micro-behaviors creates an uncannily perceptive recommendation system that other platforms are now copying. This shift is reshaping everything from news delivery to entertainment, education, and cultural consumption, turning content creation into hyper-specialized niches optimized for algorithmic engagement. While providing immediate satisfaction and personalized content, this model trades away sustained attention, serendipitous discovery, and the ability to engage with complex ideas that don't offer instant rewards.

  6. 6
    Article
    Avatar of hnHacker News·31w

    Will Amazon S3 Vectors Kill Vector Databases—or Save Them?

    AWS S3 Vectors offers 90% cost savings for vector storage but won't replace dedicated vector databases like Milvus. Instead, it fills the cold storage tier in a three-tier architecture (hot/warm/cold) that balances latency, cost, and scale. S3 Vectors excels at low-QPS scenarios and archival storage but struggles with high-performance search, frequent updates, and complex queries. The future lies in tiered vector storage where different solutions serve different performance and cost requirements.

  7. 7
    Article
    Avatar of medium_jsMedium·30w

    Don’t buy GPUs for AI

    GPUs are becoming unnecessary for most AI applications as smaller language models like Mistral 7B and Phi-3 Mini deliver practical results on CPUs. Modern processors, edge devices with NPUs, and cloud rental options provide cost-effective alternatives to expensive GPU ownership. Specialized hardware like TPUs and software optimizations through quantization are making GPUs obsolete for all but the largest model training operations.

  8. 8
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·32w

    8 Key LLM Development Skills for AI Engineers

    Outlines eight essential skills for AI engineers working with Large Language Models in production environments: prompt engineering, context engineering, fine-tuning, RAG systems, agents, deployment, optimization, and observability. Each skill covers practical techniques from crafting structured prompts to implementing monitoring systems, with emphasis on moving beyond basic prompting to building scalable, production-grade LLM applications.

  9. 9
    Article
    Avatar of weprodevWeProDev·32w

    Anthropic AI courses

    Anthropic has launched an educational platform offering structured courses on AI and machine learning concepts. The platform provides learning resources for developers and professionals looking to understand and work with AI technologies, particularly focusing on Anthropic's approach to AI development and safety.

  10. 10
    Article
    Avatar of infoworldInfoWorld·30w

    AI developer certifications tech companies want

    AI certifications are becoming increasingly valuable for developers as companies integrate AI into mainstream workflows. While not a guarantee for landing jobs, certifications from major platforms like AWS, Google, Microsoft Azure, and NVIDIA can help candidates stand out, especially for early-career developers. Industry experts emphasize that certifications work best when combined with practical experience and serve as validation of baseline competencies in rapidly evolving AI technologies. Popular certifications include AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, and Microsoft Azure AI Engineer Associate.

  11. 11
    Article
    Avatar of tdsTowards Data Science·30w

    How to Become a Machine Learning Engineer (Step-by-Step)

    A comprehensive roadmap for becoming a machine learning engineer, covering essential skills from mathematics and statistics to Python programming, SQL, machine learning algorithms, deep learning, software engineering practices, and MLOps. The guide emphasizes practical learning with specific resource recommendations for each area, highlighting that engineering skills are often more important than theoretical knowledge for career success.

  12. 12
    Article
    Avatar of bytebytegoByteByteGo·29w

    How Fine-Tuning Transforms Generic AI Models into Specialists

    Fine-tuning transforms generic AI models into specialized tools by adjusting their neural network weights for specific tasks. While training models from scratch costs millions, fine-tuning existing models like GPT or Claude costs only hundreds or thousands of dollars. The process includes instruction fine-tuning, reinforcement learning from human feedback (RLHF), and domain adaptation. Breakthrough techniques like LoRA and QLoRA have democratized AI customization by reducing memory requirements from 500GB to 20GB and enabling fine-tuning on consumer hardware, making specialized AI accessible to small organizations and researchers.

  13. 13
    Article
    Avatar of bytebytegoByteByteGo·31w

    Start Learning AI — Our New YouTube Channel is Live

    ByteByteGo has launched a new YouTube channel called ByteByteAI focused on AI education. The channel will publish weekly videos covering topics like reasoning LLMs, coding agents, prompt engineering, recommendation systems, and various AI concepts. The first video is already available with plans for regular content releases.

  14. 14
    Article
    Avatar of bytebytegoByteByteGo·30w

    Become an AI Engineer | Learn by Doing | Cohort Based Course

    ByteByteGo launches its first cohort-based AI engineering course in collaboration with Ali Aminian. The program emphasizes hands-on learning through building real-world AI applications, structured curriculum from fundamentals to advanced topics, live mentorship, and community-driven learning. The course targets skill building over theory, aiming to provide participants with a strong foundation for building AI systems.

  15. 15
    Article
    Avatar of sebastianraschkaSebastian Raschka·32w

    Understanding and Implementing Qwen3 From Scratch

    A comprehensive guide to implementing Qwen3, one of the leading open-source large language models, from scratch using pure PyTorch. The article explores why Qwen3 is popular among developers, including its Apache License v2.0, strong performance rankings, and variety of model sizes from 0.6B to 480B parameters. It provides hands-on code implementation to understand the architecture's inner workings.

  16. 16
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·29w

    Get Free Lifetime Access to Our Premium Resources

    A comprehensive 10-step roadmap for becoming a full-stack AI engineer, covering everything from coding fundamentals and Python basics to advanced topics like LLM APIs, RAG systems, AI agents, production deployment, observability, security, and advanced workflows. The roadmap progresses from beginner concepts to expert-level implementation of production-ready AI systems.

  17. 17
    Article
    Avatar of xubairZubair Ahmed Rafi·29w

    Why AI lies?

    AI models sometimes generate incorrect information because they're trained to always provide an answer rather than admit uncertainty. This behavior mirrors human psychology where people prefer giving a response over saying 'I don't know.' The issue stems from training systems that prioritize producing output over accuracy, though some newer models are addressing this through improved algorithmic approaches.

  18. 18
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·28w

    ​6 Popular Agentic Design Patterns Used in AI Products!​

    Explores six key agentic design patterns that power modern AI systems: ReAct (reasoning and action), CodeAct (direct code execution), Modern tool use (external API integration), Self-reflection (error correction), Multi-agent workflows (specialized agent collaboration), and Agentic RAG (intelligent data retrieval). Each pattern addresses specific challenges in AI agent development, with real-world examples from companies like CrewAI, Cursor, and Perplexity demonstrating their practical applications.

  19. 19
    Article
    Avatar of huggingfaceHugging Face·29w

    Gaia2 and ARE: Empowering the community to study agents

    Hugging Face introduces Gaia2, an advanced AI agent benchmark that goes beyond read-only tasks to evaluate interactive behaviors in real-world conditions. Unlike its predecessor GAIA, Gaia2 tests agents on complex scenarios including ambiguity handling, time-sensitive actions, and noise tolerance using a smartphone mock-up environment. The release includes the open-source Agent Research Environments (ARE) framework for running, debugging, and evaluating agents with structured trace recording. Current results show GPT-5 as the top performer, while temporal reasoning remains challenging for all models. The platform enables researchers to create custom scenarios and connect their own tools via MCP integration.

  20. 20
    Article
    Avatar of palindromeThe Palindrome·32w

    Correlation vs. cosine similarity

    Explores the key differences between Pearson correlation and cosine similarity, two statistical measures for quantifying relationships between variables. While both are based on dot products, correlation performs double normalization (mean-centering and variance scaling) while cosine similarity only normalizes by magnitude. Through mathematical explanations and Python simulations, the post demonstrates that these measures can yield dramatically different results depending on data scaling and offsets. Correlation is recommended when measurement units are arbitrary or different, while cosine similarity is preferred when variables share meaningful units, particularly in machine learning applications with vector embeddings.

  21. 21
    Video
    Avatar of youtubeYouTube·31w

    AI & ML Full Course 2025 | Complete Artificial Intelligence and Machine Learning Tutorial | Edureka

    A comprehensive beginner-friendly course covering artificial intelligence and machine learning fundamentals. Explores AI history from the Turing test to modern applications, explains the differences between AI, ML, and deep learning, and discusses various AI types from narrow to super intelligence. Covers Python's role in AI development, essential libraries like TensorFlow and scikit-learn, and practical applications in cybersecurity and entertainment. Includes hands-on examples and prepares learners for building intelligent systems that can make predictions and solve real-world problems.

  22. 22
    Article
    Avatar of jeffgeerlingJeff Geerling·28w

    The Emperor Has No Clothes

    The current AI market appears to be in a massive bubble, with trillions in valuations that cannot be justified by future profits. While legitimate use cases for machine learning exist, they're overshadowed by overhyped and overpriced AI products. Hardware capabilities are slowing down just as model training demands increase, creating a concerning disconnect between investment and practical value.

  23. 23
    Article
    Avatar of tcTechCrunch·31w

    After selling to Spotify, Anchor’s co-founders are back with Oboe, an AI-powered app for learning

    Former Anchor co-founders launched Oboe, an AI-powered learning platform that generates personalized courses on any topic through simple prompts. The app offers nine different learning formats including text, audio lectures, podcast-style discussions, games, and interactive tests. Built with a multi-agent AI architecture, Oboe creates courses within seconds while ensuring accuracy and personalization. Users can access free courses and create up to five monthly courses for free, with paid tiers offering 30 or 100 additional courses. The startup raised $4 million in seed funding led by Eniac Ventures.

  24. 24
    Article
    Avatar of hnHacker News·32w

    The maths you need to start understanding LLMs

    Explains the fundamental mathematical concepts needed to understand how Large Language Models work, focusing on vectors, matrices, high-dimensional spaces, embeddings, and projections. Covers vocab spaces where logits represent token probabilities, embedding spaces where similar concepts cluster together, and how matrix multiplication enables projections between different dimensional spaces. Demonstrates that neural network layers are essentially matrix multiplications that project between spaces, making LLM inference accessible with high-school level mathematics.

  25. 25
    Article
    Avatar of infoqInfoQ·32w

    Hugging Face Releases Trackio, a Lightweight Open-Source Experiment Tracking Library

    Hugging Face launched Trackio, a lightweight open-source Python library for ML experiment tracking that serves as a drop-in replacement for Weights & Biases. The library features under 1,000 lines of code, local SQLite storage with automatic Parquet backups, local dashboards by default, and seamless integration with Hugging Face ecosystem. Key capabilities include wandb API compatibility, GPU energy usage tracking, and direct integration with model cards for environmental impact reporting.

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