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  1. 14 hr ago

    I Built a Tiny GPT That Speaks Sanskrit in a Weekend — Here’s What Broke

    This story was originally published on HackerNoon at: https://hackernoon.com/i-built-a-tiny-gpt-that-speaks-sanskrit-in-a-weekend-heres-what-broke. The off-the-shelf models are bad at Sanskrit largely because of tokenization and data scarcity Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #transformers, #sanskrit, #from-scratch-transformer, #gpt, #build-your-own-gpt, #tokenization, #llms, #hackernoon-top-story, and more. This story was written by: @amitshukla. Learn more about this writer by checking @amitshukla's about page, and for more stories, please visit hackernoon.com. I gave myself a weekend and a constraint: build a GPT small enough to understand completely, but on a language that would actually fight back — Sanskrit. I have a pile of Devanagari text and an NVIDIA DGX Spark sitting on my desk, so why not?

    I Built a Tiny GPT That Speaks Sanskrit in a Weekend — Here’s What Broke

  2. 14 hr ago

    AI Literacy Starts at Home: How to Use AI Agents in Everyday Life

    This story was originally published on HackerNoon at: https://hackernoon.com/ai-literacy-starts-at-home-how-to-use-ai-agents-in-everyday-life. This paper, titled "AI Literacy Starts at Home: How to Actually Use AI and AI Agents in Everyday Life," argues that the real value of AI now lies in using AI ag Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-literacy, #ai-agents, #ai-for-everyday-tasks, #ai-adoption, #agentic-ai, #ai-workflows, #ai-hallucinations, #human-in-the-loop-ai, and more. This story was written by: @SohamRijal_hg0vnl18. Learn more about this writer by checking @SohamRijal_hg0vnl18's about page, and for more stories, please visit hackernoon.com. AI is shifting from chatbots that answer questions to agents that complete multi-step tasks — and most people (and companies) are still stuck using it as a Q&A tool rather than a real workflow assistant, per McKinsey. To use it well: pick real recurring tasks (not toy demos), follow Goal → Context → Instructions → Output → Verify → Improve, and always verify — hallucination rates hit 22–94% in Stanford's 2026 benchmark when models are told a false claim by a confident user. Use agents for chores with several steps, keep sensitive data and real-world actions (payments, sending emails) behind human approval, and stay current by re-testing tools quarterly rather than chasing "top 10 AI tools" lists. Bottom line: AI agents are real and growing fast (Gartner: ~40% of enterprise apps by end of 2026), but still error-prone and overhyped in the short term — the people who benefit are the ones who use it deliberately and keep a human check on the output.

    AI Literacy Starts at Home: How to Use AI Agents in Everyday Life

  3. 1 day ago

    The AI Writing "Witch Hunt" Is a Huge Waste of Everyone's Time

    This story was originally published on HackerNoon at: https://hackernoon.com/the-ai-writing-witch-hunt-is-a-huge-waste-of-everyones-time. The "AI witch hunt" misdirects anger at the tool. AI didn't corrupt writing; it exposed human mediocrity and hypocrisy. Here is why the fight is flawed Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-writing, #ai-generated-content, #good-vs-bad-ai-writing, #ai-detection, #ai-disclosure, #ai-authorship, #ai-content-detection, #hackernoon-top-story, and more. This story was written by: @zverevspace. Learn more about this writer by checking @zverevspace's about page, and for more stories, please visit hackernoon.com. The article argues that AI-generated writing should not be rejected simply because it involved AI. The real editorial issue, in the author’s view, is when people pass off weak or fully AI-generated work as their own without honesty or accountability.

    The AI Writing "Witch Hunt" Is a Huge Waste of Everyone's Time

  4. 1 day ago

    The Demotion Ladder: A Year of Governing Claude Code

    This story was originally published on HackerNoon at: https://hackernoon.com/the-demotion-ladder-a-year-of-governing-claude-code. Why prose rules fail for AI coding agents, and how a demotion ladder of hooks, write partitions and build-enforced checks holds the line instead. 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, #claude.md, #agentic-coding, #ai-code-review, #claude-code-governance, #llm-compaction, #hackernoon-top-story, and more. This story was written by: @fedoryshchev. Learn more about this writer by checking @fedoryshchev's about page, and for more stories, please visit hackernoon.com. Agent rules fail not because the ruleset is too big, but because compliance decays within a session, compaction drops constraints, and prohibitions rot. To hold a rule, demote it down a ladder: delivered prose, write partitions, command allow-lists, tests as the definition of done, and states the build makes unrepresentable.

    The Demotion Ladder: A Year of Governing Claude Code

  5. 2 days ago

    The SLM Revolution: Taking a Look at Why Fit Beats Force

    This story was originally published on HackerNoon at: https://hackernoon.com/the-slm-revolution-taking-a-look-at-why-fit-beats-force. For the last few years, AI engineering has operated under a surprisingly simple assumption: Bigger models are better models. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #small-language-models, #llms, #slms-vs-llms, #what-is-a-slm, #slm-explained, #ai-models, #hackernoon-top-story, and more. This story was written by: @vishalchandak0212_n4vr52q8. Learn more about this writer by checking @vishalchandak0212_n4vr52q8's about page, and for more stories, please visit hackernoon.com. There isn't one universally accepted parameter-count boundary that separates an SLM from an LLM. Different researchers and vendors use different definitions. Some emphasize parameter count. Others focus on memory, latency, deployment environment, or computational constraints.

    The SLM Revolution: Taking a Look at Why Fit Beats Force

  6. 4 days ago

    MCP Explained: Most AI Agent Builders Are Rebuilding What This Protocol Already Solves

    This story was originally published on HackerNoon at: https://hackernoon.com/mcp-explained-most-ai-agent-builders-are-rebuilding-what-this-protocol-already-solves. Most AI agent builders are solving the N x M integration problem from scratch. MCP already solves it. Here is what it does and when to use it. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #mcp, #model-context-protocol, #ai-agents, #agentic-ai, #llms, #devops, #platform-engineering, #open-source, and more. This story was written by: @santoshmahale. Learn more about this writer by checking @santoshmahale's about page, and for more stories, please visit hackernoon.com. MCP is an open protocol that standardises how AI agents connect to tools, solving the N x M integration problem. Think of it as USB-C for AI agents. Know when to use it and when to build without it.

    MCP Explained: Most AI Agent Builders Are Rebuilding What This Protocol Already Solves

  7. 4 days ago

    AI Tokens: How They Work, How to Count Them, and How to Stop Wasting Them

    This story was originally published on HackerNoon at: https://hackernoon.com/ai-tokens-how-they-work-how-to-count-them-and-how-to-stop-wasting-them. You use ChatGPT or Claude every day. But do you know why it sometimes forgets what you said earlier? Or why your API bill spiked? One word: tokens. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #artificial-intelligence, #machine-learning, #tutorial, #developer-tools, #beginners, #tokenization-costs, #gpt, #claude, and more. This story was written by: @cloudsavant. Learn more about this writer by checking @cloudsavant's about page, and for more stories, please visit hackernoon.com. You use ChatGPT or Claude every day. But do you know why it sometimes forgets what you said earlier? Or why your API bill spiked? One word: tokens.

    AI Tokens: How They Work, How to Count Them, and How to Stop Wasting Them

  8. 5 days ago

    Let's Build Our Own LLM (Part 1): Tokenization and Data Prep

    This story was originally published on HackerNoon at: https://hackernoon.com/lets-build-our-own-llm-part-1-tokenization-and-data-prep. How LLMs turn text into numbers: BPE tokenization explained step by step, why your choice of tokenizer shapes model quality, and building a data pipeline. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-engineering, #llms, #tokenization, #byte-pair-encoding-(bpe), #ai-data-pipeline, #bpe-algorithm, #huggingface-tokenizers, #hackernoon-top-story, and more. This story was written by: @jayrajch. Learn more about this writer by checking @jayrajch's about page, and for more stories, please visit hackernoon.com. LLMs don't see words, they see tokens, chunks of text learned by an algorithm called Byte Pair Encoding that repeatedly glues the most frequent character pairs together. A tokenizer trained on Reddit will shred "myocardial" into meaningless fragments; one trained on medical text keeps it whole. That choice ripples through everything. This article walks through BPE merge-by-merge with a toy corpus, compares how GPT-4, LLaMA-2 and BERT tokenize clinical text, then covers the data pipeline, deduplication, quality filtering, and the token-count math you should do before spending a dollar on GPUs. Working Python code for all of it.

    Let's Build Our Own LLM (Part 1): Tokenization and Data Prep
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