Tag: #ai

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• The Palantir Ontology — reading, from the official docs, the operational layer that puts "action" on top of data ♪ Listenable

  What Palantir calls an ontology is not a philosophical term but a layer of the product. Six building blocks — objects, properties, links (semantics) and actions, functions (kinetics) — the path from a dataset to an objec

  2026-09-11 · 10 min read #palantir#ontology#foundry#aip#data-platform

• Anatomy of an AI agent harness — 35 files that make the rules enforce themselves

  Same model, yet in one repository the accidents repeat and in another they do not. The difference is the harness. A file-by-file dissection of the harness in a repository where an AI accumulated 1,491 commits alongside a

  2026-09-11 · 13 min read #ai#agent#harness#claude-code#ci-cd

• How Reasoning Models Are Built — From Chain-of-Thought to GRPO, in Twelve Papers

  o1, R1 and QwQ are not new architectures. They are long output optimised toward a gradeable objective. This traces the lineage through twelve papers — Chain-of-Thought (2022), STaR, PRM, DeepSeek-R1's GRPO, s1's budget f

  2026-09-06 · 12 min read #ai#llm#reasoning#reinforcement-learning#grpo

• AI for Everyone, Part 6 — Drawing Digits From Words With a 1.11M Diffusion Model

  We built a conditional diffusion model with 1.11 million parameters that draws a 0 when you type "zero". The forward process that adds noise is a single formula; the reverse process that restores the image is that same l

  2026-08-24 · 8 min read #ai#diffusion#ddpm#generative#pytorch

• AI for Everyone, Part 5 — Colourising Photos With a 0.47M U-Net, and Why the Colours Came Out Washed Out

  The smallest model in this series — a 472K-parameter U-Net — restored colour to greyscale CIFAR-10 images. Shapes survived intact, but the colours came out noticeably washed out. That is not a capacity problem; it is a c

  2026-08-23 · 7 min read #ai#computer-vision#unet#colorization#pytorch

• AI for Everyone, Part 4 — Captioning Images With 1.37M Parameters, and Why Part 3 Bug Was Absent Here

  We joined a CNN encoder to a transformer decoder and captioned Fashion-MNIST images. With 1.37 million parameters and ten minutes of training, the label hit rate reached 91%. This part looks at what cross-attention does

  2026-08-22 · 7 min read #ai#captioning#multimodal#transformer#pytorch

• AI for Everyone, Part 3 — Loss of 0.0017, Accuracy of 7.5%: The Culprit Was One Padding Slot

  We built a VQA model — one that answers questions about an image — with 1.48 million parameters. Training loss fell to 0.0017 while accuracy sat at 7.5%, worse than guessing. The cause was not the model but a single line

  2026-08-21 · 8 min read #ai#vqa#multimodal#debugging#pytorch

• AI for Everyone, Part 1 — Training a 16M-Parameter Language Model From Scratch in 15 Minutes

  Training a language model from scratch on a single GPU. With the TinyStories dataset and a 16-million-parameter decoder-only transformer, we produced readable English fairy tales in 15 minutes. We look at why the causal

  2026-08-19 · 9 min read #ai#llm#transformer#pytorch#hands-on

• English for AI Engineering Meetings: Saying You Do Not Know Without Losing Credibility ♪ Listenable

  The English an AI team meeting needs most is not the language of certainty but the language that makes uncertainty credible. Reporting eval results together with their limits, flagging a regression whose cause is still u

  2026-08-16 · 31 min read #english#business-english#meeting#ai#llm

• What Stays Expensive — Four Things That Gain Value When Generation Gets Cheap

  Answer the question "what should engineers prepare for" with a list of tools and the answer is stale in two years. This piece proposes a different axis: the skills that hold value are the ones that stay expensive to veri

  2026-08-15 · 6 min read #career#skills#ai#craft#engineering-culture

• What We Actually Know and What We Do Not — Handling Career Predictions

  Writing about the future of software work comes in two flavors: reassurance that says do not worry, and warning that says prepare now or fall behind. Both state predictions nobody can support as though they were facts. T

  2026-08-15 · 6 min read #career#ai#uncertainty#forecasting#job-security

• Why Juniors Are Especially Anxious — The Entry Ladder Has Changed

  A junior engineer's anxiety comes from position, not personality. It is the layer most often named as replaceable, the layer with the least evidence behind it, and the layer with the most people to compare against. This

  2026-08-15 · 6 min read #career#junior-developer#ai#learning#entry-level

• Working With AI Tools as a Skill — Where the Delegation Line Sits and How to Verify

  Saying someone is good with AI tools carries no information until you know what they delegated and how they judged the result. This piece covers the criterion that draws the delegation line by verification cost rather th

  2026-08-15 · 7 min read #career#skills#ai#verification#engineering-practice

• Korean Dev Blog Curation 3 — AI and ML in Practice, 14 Posts I Opened and Checked

  Fourteen Korean-language posts on applying AI and ML in production, selected for being concrete and reproducible. They cover an end-to-end LangChain RAG pipeline, how semantic search actually works through embeddings and

  2026-08-12 · 12 min read #curation#큐레이션#ai#llm#rag

• What Is a Forward Deployed Engineer (FDE) — the Engineer Stationed at the Customer Site

  Palantir invented the title, and now OpenAI and Anthropic are racing to hire for it: Forward Deployed Engineer, or FDE. This post pins down what the role actually is — an engineer whose coordinate system is the customer

  2026-08-12 · 6 min read #career#fde#forward-deployed-engineer#ai#job-search

• Choosing Speech Models: Practical Criteria for STT and TTS

  Unlike text models, speech models are chosen after the language coverage, audio length constraints, real-time requirement, and diarization need are already fixed. This post lays out the parameters, licenses, language cov

  2026-08-12 · 7 min read #ai#huggingface#open-source-llm#speech-to-text#text-to-speech

• Choosing Open Text Generation Models by Size Class

  When picking an open text generation model, size is not a quality tier — it is a deployment constraint. This post splits the field into on-device small, single-GPU mid, and server-class large, listing the parameters, con

  2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#text-generation

• Open Models That Support Korean, and the Cost of Tokenization

  A line saying a model supports Korean means it processes the language, not that it is good at it. This post lays out the card values read on 2026-08-12 for Korean-focused, bilingual, and multilingual open models, explain

  2026-08-12 · 6 min read #ai#llm#huggingface#open-source-llm#korean-nlp

• Licenses and Deployment: Reading the Types and Verifying Quantized Builds

  The short identifier in an open model license field is a table of contents; the actual terms live in the full text. This post organizes the license types actually encountered on 2026-08-12, what community licenses and no

  2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#license

• Choosing Code Models: Completion vs Chat, FIM, and Licenses

  Code models come in two different products: the completion model that fills the cursor position inside an editor, and the conversational model that answers questions. This post organizes the parameters, context, fill-in-

  2026-08-12 · 7 min read #ai#llm#huggingface#open-source-llm#code-llm