Things I learned

This week, I learned: Embedding models can be fine-tuned. Example: #TODO Agentic RAG (Ravi Theja, LlamaIndex) RAG via top-k retrieval fails with summarization => need to read all chunks comparison: compare product X vs Y => need to split and re-combine structured analytics. e.g. most expensive employees => Text2SQL first multi-part questions. e.g. Tell me about speed of model X AND cost of model Y and recommend => need to split and re-combine RAG failures: It’s single shot. No query planning. No tools. No correction. No memory. Agents that help in RAG Route to the right tool E.g. retrieve via vector top-k search or vector summary search or keyword search or combination? One-shot query planning E.g. Break query into multiple specific queries. RAG those. Then combine. #TRY - maybe in DocSearch Tool use E.g. Schema retrieval, Text2SQL, Calendar, Chat, APIs, Search, etc. Agent orchestration ReAct: An agent reasoning loop. Reason + Act. {Thought, Action, Action Input, Observation}*. Orchestrate tools with a prompt Multi-agent task solver: Llama agents Instead of a single agent loop, use different agents. Also allows parallelization Allow services to register. (MS TaskWeaver stores tool descriptions in YAML) LlamaHub Tools has ideas for agents Notes on LLM Fine-Tuning Rouge 2 and Bleu and such metrics are NOT good. Create you own benchmarks Non-PEFT fine tuning needs 6X GPU RAM. Optimizer states, Gradient, Activations are the overhead. PEFT is about tuning a subset of parameters. LORA adds additional weights without updating the model. It’s a low rank matrix multiplication. You can change these adapters in runtime. Saves space. Fast to train Quantization: Stick to bitsandbytes or AWQ (may be a bit better) QLORA = Quantization + LORA Predibase has open-sourced Lora Adapters in “Lora Land”. Existing adapters are pretty good. ghcr.io/predibase/lorax:main Docker image works on Docker compose to run locally. devices: on Docker Compose lets you specify NVIDIA GPU devices Locust is a HTTP load testing lib in Python Techniques for inference optimization Dynamic adapters: Loads right LORAX adapters WHEN a request comes in Multi-adapter batching: Process all inputs in parallel on the same GPU, but different users are post-processed using different adapters Notes from a 4-hour flight: What We’ve Learned From A Year of Building with LLMs Strategy IS IT TOO HARD/EXPENSIVE? Log it. LLMs are getting cheaper and better. WILL OPENAI BUILD IT? If so, wait for it instead of building. HAS A STARTUP BUILT IT? If so, use it instead. It’s a generic use case there’s no point re-inventing. FOCUSED USE CASES over generic. Build trust by starting small. Tools for LLM Ops (feedback): LangSmith, Log10, LangFuse, W&B Weave, HoneyHive #TRY Human in the Loop is about humans evaluating model outputs. That’s different from AI in the loop, human in the center, where AI accelerates human output (like Github Copilot) Operations CHECK EMBEDDINGS DRIFT over time. Users might be input-ing different things than before. LOG AND REVIEW everything. Instructor coaxes structured output from LLM APIs. #TRY IMPLICIT FEEDBACK collection is easy. Just let users edit stuff. #TRY Tactical Try n-shot prompting (n=5-12) before bigger models. #TRY Always structure for output: Markdown, XML/HTML tags. Combine RAG with Keyword search. It reduces user frustration in edge cases. Prefer multiple small prompts to one big prompt. Do X. Then Y. Then Z. Jitter prompts for diversity beyond temperature. LLM-as-judge works better when comparing outputs (not rating 1 output). Keep length similar (LLMs prefer wordiness). Swap order and compare. Allow for ties. Ask for reason FIRST. Hermes: A Text-to-SQL solution at Swiggy “Hermes performed significantly better for charters with well-defined metadata and a relatively smaller number of tables.” “We collect feedback on the accuracy of the returned query from stakeholders directly within the Slack bot.” How I use AI and “Replacing my right hand with AI” EMBED in every app/workflow. E.g. Auto-fix spellings. Auto-review code. Auto-ask LLM on errors and apply patch! Auto-search for answer, assess, continue. PERSIST. Stick with the LLM to the end. Don’t fix it yourself. It’s faster. #TRY INTERVENE FAST. If an LLM can’t solve it by itself in 2 tries, it needs in-depth help. APP-IFY one-off tasks. Disposable tools. “Write web-app to convert JSON to tab-delimited.” “Extract fields as a table.” “Diff JSON.” #TRY BEST language/frameworks preferred. CUDA in Python. Rust. C. Raspberry Pi. Arduino. Bluetooth. Modern ESM/JS. #TRY TEACH examples. “Here’s the LLM Foundry API.” “Here’s how to use gramex.data.” DUMP entire code. Models can handle it. Refactoring to SQLAlchemy 2, Pandas 2. API Documentation. Test case generation. #TRY ASK for features & packages. Docker without root access. GPU access inside docker. Windows CLI-only C++ compiler. TEST CASE writing. #TRY SPEC IN DETAIL. Use these libraries. Write like this: code example. SPEC USAGE in detail. “I will just pipe it into sqlite”, or “I will just run ffmpeg -i filename [YOUR OPTIONS]. Describe the UI, API input/output, data structure, and internal data structure. HELP on usage. “ffmpeg to get audio.mp3”. My benchmark for large language models LLM(text) is a useful function to have in JS and Python too. Useful as a simple pip install llmfoundry Allow images, files in LLM() Current list of #IMPOSSIBLE (or hard) things for LLMs Translate technical documents to Dutch – because they don’t understand the technical terms well Translate large documents (JSON to XML, English to Chinese, Python to Rust, Wrong to right spelling) – because the output tokens are limited micro-agent generates test cases first when asked to build an app. Then it iterates until the test cases pass. Alternative interfaces to YouTube: Piped.video, CloudTube, Invidious, NewPipe, FreeTube Deepseek Context Caching reduces price to 1.4 cents/MTok for portions of chat messages that are repeated. That’s a 10X reduction for long conversations!

This week, I learned: Assisted generation uses a faster LLM to generate text and a better (tokenizer-compatible) LLM to validate it. This makes it faster. E.g. Gemma 2 2b with Gemma 2 27b Power Toys has an Advanced Paste that uses OpenAI to paste as Markdown or JSON! Interest Turing complete languages: find + mkdir, maybe sed and awk Minecraft’s Redstone Circuits Conway’s Game of Life Cellular Automata Rule 110 Magic: The Gathering SQL Excel Rev.ai does a good job of diarization. Cost: 2 cents per minute. Update: 6 Jun 2025. Cost: 0.33c/min Ref

This week, I learned: Speech editing in audio files is a thing. Speech Editing Toolkit and Descript GPT 4o Mini is almost as good as GPT 4o in the LMSYS leaderboard. Llama 3.1 400B model and Mistral 2 Large are yet to be evaluated. If LLMs can generate any text, and text can describe the real world, we can rapidly generate “artifacts” that generate: 3D Printable Models: STL (Stereolithography): Defines the surface geometry of 3D objects using triangular facets. OBJ (Wavefront OBJ): Describes 3D geometry including vertices, textures, and normals. X3D: An XML-based file format for representing 3D computer graphics. Vector Graphics: SVG (Scalable Vector Graphics): Defines vector-based graphics in XML format, useful for illustrations, diagrams, and user interface elements. CAD Drawings: DXF (Drawing Exchange Format): Represents CAD data, including shapes, lines, and curves, used in engineering and architecture. Circuit Designs: KiCAD: An open-source software suite for Electronic Design Automation (EDA), which uses various file formats like PCBNew and EESchema to represent circuit designs. Blueprints and Architectural Designs: GML (Geography Markup Language): Encodes geographical features and spatial information. CityGML: A specific GML application schema for modeling and exchanging 3D city models. Molecular Structures: PDB (Protein Data Bank): Describes the three-dimensional structures of molecules. CML (Chemical Markup Language): An XML-based standard for representing molecular data. Robotics and Automation: URDF (Unified Robot Description Format): Defines the physical configuration of a robot, including joints, links, and sensors. COLLADA (Collaborative Design Activity): An XML-based schema to describe digital assets for 3D applications, often used in robotics. Geospatial Data: KML (Keyhole Markup Language): Used for geographic data visualization, primarily in Google Earth. GeoJSON: A format for encoding a variety of geographic data structures using JSON. Mathematical Markup: MathML (Mathematical Markup Language): Describes mathematical notation and captures both its structure and content. Music and Sound: MusicXML: Encodes sheet music in a structured format that can be easily shared between different music notation software. Documents and Text: DocBook: A semantic markup language for technical documentation. Markdown: A lightweight markup language with plain text formatting syntax. Biological Data: SBML (Systems Biology Markup Language): Represents computational models of biological processes. PhyloXML: An XML format for representing phylogenetic trees. Game Development: FBX (Filmbox): A file format for 3D animation that can hold information about the geometry, textures, and animations. VRML (Virtual Reality Modeling Language): Describes interactive 3D objects and worlds. Data Visualization: ChartML: Encodes charts and graphs in a structured format. D3.js (Data-Driven Documents): Uses HTML, SVG, and CSS to bring data to life with interactive visualizations. Building Information Modeling (BIM): IFC (Industry Foundation Classes): Describes building and construction data. Textiles and Fabrics: LoomML: Represents the design and structure of woven fabrics. Augmented Reality and Virtual Reality: ARML (Augmented Reality Markup Language): Defines how augmented reality applications should behave and what content they should display. VRML (Virtual Reality Modeling Language): For describing interactive 3D objects and worlds. Medical Imaging and Health Data: DICOM (Digital Imaging and Communications in Medicine): Encodes medical imaging data. HL7 (Health Level 7): A set of standards for the exchange of information between medical applications. Simulation Data: FMI (Functional Mock-up Interface): Represents and exchanges dynamic simulation models. SBML (Systems Biology Markup Language): For computational models of biological processes. Sound and Audio: MML (Music Markup Language): For encoding music notation and performance information. SoundFont: A file format for defining musical instrument sounds. Animation and Visual Effects: BVH (Biovision Hierarchy): Encodes motion capture data. Alembic: A computer graphics interchange framework primarily for exchanging animation and visual effects data. Textile Patterns: WIF (Weaving Information File): Describes weaving patterns and structures. Knitting Markup Language: Encodes knitting patterns in a structured format. Scientific Data: CDF (Common Data Format): Used for storing scientific data. NetCDF (Network Common Data Form): Supports the creation, access, and sharing of array-oriented scientific data. Photography and Imaging: XMP (Extensible Metadata Platform): Used for embedding metadata in digital images and other media files. Construction and Engineering: LandXML: For civil engineering and land surveying data. gbXML (Green Building XML): Facilitates the transfer of building data for analysis of energy and environmental performance. Packaging and Retail: BPL (Barcode Product Labeling): Encodes information for product packaging and labeling. GS1 XML: Used for electronic business messaging, including product identification and tracking. Typography and Font Design: UFO (Unified Font Object): A format for storing font data. SFNT (Spline Font): Encodes scalable font information. Product Data Management: PLMXML (Product Lifecycle Management XML): Used for sharing product data across PLM systems. GPT 4o Mini can be fine-tuned! Awesome PaaS lists self-hosted deployment platforms. Piku - similar to Dokku – is promising.

This week, I learned: GPT For Work has a set of useful spreadsheet LLM functions Xata offers a free PostgreSQL tier with REST API Mamba now uses mambaforge as the default installation, i.e. conda-forge is the default and only channel! Update: 6 Jun 2025. Mambaforge is sunset as of 29 Jul 2024. Conda-forge now uses Miniforge as the standard installer Ref conda-forge.org. Users should switch to Miniforge instead. nginx supports a load-balancing method least_conn which is far better than the default round-robin. #IMPOSSIBLE LLMs cannot provide a bounding box of objects in images. (Maybe Florence 2 can). Update: Mar 2025. Gemini has good timestamps and bounding boxes Models gently grow in capability. It helps to maintain an impossibility list that steadily gets invalidated. Ref Github Copilot internals walks through how Copilot constructs its prompts

This week, I learned: Carlton’s TDS session Always create a new venv via VS Code when starting a training session. Helps reproduce issues (though I could use Colab instead) Create an empty .ipynb notebook and double-click it. That’s another way (though slower) to open a Jupyter notebook Share Parrish Knowledge Project podcast. Three generations of wealth There is a big difference between liking animals and being a vet. Between liking education and being a teacher. Even if no one reads your writing, you benefit from the writing. Emotional.crises like 9/11 or Covid are far easier for markets to recover from Hidden brain podcast. White trying to hard can back fire on you Sometimes conscious thinking makes our automated responses of sports music, dance are great examples Instead, SURRENDER to something outside of you. Like playing with kids. Exercise also sends blood away from brain. Drugs. ChatGPT. It’s called Ue in Chinese philosophy A quick check on the pricing of text to speech models OpenAI TTS: $15/1M chars Ref Deepgram Aura: $15/1M chars Ref Elevenlabs Scale: $165/1M chars Ref Google TTS Neural2: $16/1M chars Ref Azure AI Speech: $15/1M chars Ref AWS Polly Neural TTS: $16/1M chars Ref

This week, I learned: Predibase uses LORAX to run multiple fine-tunings of a base model in a single GPU via adapters. Ref

This week, I learned: Amara’s law: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” LLM Patterns include Evals, RAG, Fine-tuning, Caching, Guardrails, Defensive UX, Collect feedback. Notably: Defensive UX: Microsoft, Google, and Apple have guidelines for Human-AI interactions Collect feedback: Explicit and implicit Rouge and Context Precision are metrics to evaluate LLM responses that serve as a starting point – but not sufficient, usually Any word with the letters izehsglbo can be spelt on a calculator. That includes Hobbes (538804)! Via Calculator spelling Tor Browser + DuckDuckGo is good for torrent searches. Maybe the Dark Web IS the original Internet. The ad-free hacker web

This week, I learned: Luma Labs Dream Machine generated videos. It’s free and is of reasonable quality. Update: 6 Jun 2025. Costs $10/month LLM DataHub has LLM training datasets, regularly updated From Dan Becker on running a workshop Answer questions at the end, not in parallel in a chat, to avoid distraction Have fewer words in slides when presenting. It’s less distracting Morgan Housel Shane Parrish podcast Risk is what stops you from achieving YOUR goals. What’s risky for me may not be risky for you The lesson from compounding is that you want to optimize for duration, not return. That’s what does the heavy lifting. Survival, consistency, long term - these matter. The performance does NOT matter.

This week, I learned: httpretty can mock ALL Python HTTP libraries Japanese pray to dead parents instead of gods. The dead are preserved in plates by priests. Japanese are generally non religious Looks like GPT-4o is using CNNs to create vector embeddings of images, with images gridded into a 1x1, 2x2, etc. PLUS OCR. Ref The sum of a sinusoidal series is like a spirogram. Spinning circle linked to another and so on https://www.andreinc.net/2024/04/24/from-the-circle-to-epicycles

This week, I learned: Modal.com seems of offer reasonably priced GPUs Combining vector search and keyword search with reciprocal rank fusion seems to work well for RAG. Ref Knowledge Project podcast. Morgan Housel Differences of opinion exist because of different stories arising from origins and experiences. We are not debating facts. We are debating life lessons! Solution: hear their anecdotes. The stories that taught them their lessons. AI reporting templates are a trend. Domain expertise comes in via structuring the report template and associated prompts. Some audio embedding models: unoti/voice-embeddings, retkowsky/audio_embeddings, pyannote/embedding (for speaker similarity), and more. Hidden Brain podcast: Innovation 2.0: The power of less Subtraction is hard because we are biologically and economically wired against it. It’s also hard because there are fewer markers of subtraction. Additions are natural markers / triggers. Marie Kondo suggests keeping only what sparks joy #POST I tried Undermind.ai - an agent that researches for you. It guides you to ask a detailed question, spends 2-3 minutes finding the answer, and provides detailed results. But it’s worth the wait. It’s a good alternative to quick validations on SciSpace. For popular results, search actually makes results worse! When not to trust language models Perception of fluency and usefulness are NEGATIVELY correlated in LLM! Evaluating Verifiability in Generative Search Engines GPTs are now available to non paying users. Apparently for a few weeks! Everyone also has limited access to GPT-4o. Discussion with Anand Explore BBC Microbit Everyone should get a Raspberry Pi! Watch 2 minutes paper on YouTube More LLM routers: LiteLLM: Open source, OpenAI compatible, 100+ LLMs RouteLLM: Open source, OpenAI compatible, automatically routes based on cost OpenRouter: OpenAI compatible API, several models Unify: Supports many models Portkey: Supports popular providers Martian: Limited set of models d-id and Heygen can modify videos of a person.

This week, I learned: My home WiFi is on WiFi 6. This supports beam-forming which increases range by “focusing” on devices! Predibase lets you run fine-tuned models at the same price, on a per-token basis. 25c/MTok up to 21B models. That’s sames as Claude 3 Haiku, but with fine-tuning. RunPod’s vLLM endpoint lets you run any HuggingFace LLM with an OpenAI API priced on usage (serverless) not on idle time. “Autoscaling to 0”. Portkey is an LLM router

This week, I learned: In Scandinavia, Århus comes after Zürich because Å is a different letter. It was added by the Dutch after WW2 to distance themselves from the Germans. via Zalgo text is where we combine multiple Unicode combining characters Artificial Analysis benchmarks LLM APIs on speed, cost, and quality.

This week, I learned: Radio free Xp podcast. Nudge 61 always announce first before doing. Give people time to plan comment and react. That gets you alignment without sacrificing freedom. give information, not orders. When someone is parking a car, tell them how much space they have, don’t tell them to start stop or how much to turn left it’s almost impossible to change the culture if you’re not the boss

This week, I learned: Hidden brain podcast. Innovation 2.0 solve your own problem. Don’t solve other people’s problems. This helps you pick what you’re good at affordable losses. Make sure you survive borrow others’ spares. spare time, scrap data, anything others don’t use. If you can monetize it, you can pay them back focus on the controllable. Ignore what’s outside your control don’t even waste time on it curl supports globbing, emails Beetrove is a ranking of the popularit of OpenAI GPTs Gemini Prompt Guide has detailed examples of how each role can use Gemini ESLint’s new flat configuration does not support package.json

This week, I learned: Tough prompt to test: Gr brx vshdn Fdhvdu flskhu? is a quick way to assess LLM capability. Ref Cheap cloud GPU services thread on Twitter lists: Runpod (17) Vast.ai (17) Modal Labs (8) fly.io (4) LightningAI (4) Colab (4) AkashNet (4) Lambda Labs (4) ShadeFormAI (3) Mac Mini (3) Tensor Dock (2) Hetzner (2) BrevDev (2) JSR lets you publish Deno packages that can be imported by npm via. It also auto-evaluates documentation and scores it! via Snowflake Arctic Cookbook explains how mixture of experts models work A long list of LLM courses online Embeddings can be averaged. So, to embed large documents, average the embeddings of their chunks! OpenAI suggests this.

This week, I learned: Effort engine introduces “effort” as a parametrizable way to speed up LLMs with a quality trade-off. Works on Mistral for now. Many arts demand devotion. Devoting unrestricted time is part of that. 16 hours of practice a day is not uncommon. Sessions don’t start and end on time. Instruments take a lot longer to learn than vocal music. The instrument needs to become an extension of you. Tests and homework have a purpose. It helps people figure out whether they’ve learnt. So: Write tests that make people think! Like DuckDB workshop Share a list of exercises that people can explore People need to explicitly be INVITED, and potentially IN PERSON, before they will engage with something new. For example, no one posted to [email protected] until the VIA Talks session where we got them to post. For example, having one day at IITM mandatory (especially early in the course) gets online students familiar with TAs. They understand that TAs actually help, at high quality. That they can use Discord. What makes Delhi students more assertive? How can we inculcate that in others? jsr-io/migrations is a great example of database migrations. Shape Detection API in the browser detects QR codes, face bounding boxes, Browsers also natively support blurring and face tracking. via Lessons after half a billion GPT tokens for GPT-4: Vague instructions are better than over-specifying Avoid libraries like Langchain. APIs are stabler 1 token = 3 characters is good enough GPT4 doesn’t hallucinate much, except it does a poor job of saying “I don’t know” or “There’s no such data” (the null hypothesis) Keep the output down to 10 items or so if you’re listing. For longer lists, have it explicitly enumerate Don’t worry about niches. Just wait for GPT5 #WRITE GPT clearly prefers 42 as a random number. #WRITE fal.ai “animates” pictures, creating videos. It made one from my talk. I morphed into various somewhat similar people rapidly in a 2-second span. Very promising, and far from good. llmsherpa extracts PDFs using LLMs. It has errors but it preserves hierarchy, extracts tables well, and retains image coordinates. Via +91 90031 35354 ~Vetrivel PS www.web.sp.am is a content farm that’s getting hit by OpenAI. Highlights how easy it is to create content farms, and therefore “easy” it can be to introduce bias into LLMs. OpenAI supports batching requests. Didn’t know that. Marvin provides Python decorators to create AI functions. Pretty intuitive! Outlines generates structured test with LLMs. It uses the ⭐ logit_bias trick to limit choices in output. See get_choice() Lemur from Assembly.ai does real time call transcription and summary W3C is exploring ways to allow web pages to train LLMs, to flag content as AI generated, etc. Data Provenance Explorer lists open datasets used to train LLMs. Summarize.tech summarizes YouTube videos. #WRITE Stable Audio 2.0 generates 3 min of music from a prompt. I tried Bollywood Tamil film background music. Dark, soulful and Horror movie background. Drums starts darkly. Build up to a crescendo of intense chaos.. Great that it managed, but not great music. Somewhat stereotyped. I need to learn how to prompt better. BTW, Udio is another such. Harpa.ai is a well designed Chrome extension / plugin that can chat with or automate any page. Due to in-context learning, giving 100s of examples in the prompt can teach LLMs to jailbreak. Ref With RAG on search becoming big, search APIs are growing. serper.dev, you.com, searxng being examples.

This week, I learned: Prashant Pandey: we need to prepare before every meeting. Something to teach VS Code Select any code and command Explain this to understand the code %something in command bar searches ACROSS files for a term. Exactly like Ctrl+Shift+F Copilot has an Inline Chat: Start in Terminal (that needed me to unbind Ctrl+I in bash to work) Ctrl+2 opens a second window on the side. Ctrl+1 goes back to the first window Terminal: Open Detected Link lets you scroll through detected (file) links in terminal Terminal sticky scroll is transparent. (But Terminal stick scroll isn’t working for me.) Copilot uses last 10 commit messages, Jupyter notebook kernel state (variables) as additional context 1.88: supports locked scrolling to sync scrolling of side-by-side windows fsspec is used by csvbase, Pandas, etc. to implement file system protocols like s3fs, gcfs, etc. SQLime is a SQLite client / playground on the browser! Do nothing. Then do less Humans have a bias against inaction. Hence a strategic advantage. What can you cancel today? Humans have a bias against subtraction or removal. That too is a strategic advantage. What can you remove today? Humans have a bias against constraints. That’s a strategic advantage. What constraint can you embrace? No Yay! When declining something, add it your calendar so that when the time comes you can say yeah I got this time back

This week, I learned: CSS nesting is now available in browsers Cold starts in AWS Lambda: serverless functions stay alive for 5-7 min. All languages are fast but Docker is slow. More npm packages slow start dramatically. WiFi only works when it’s raining because a tree was obstructing the signal but was weighed down when raining! Good reasons why finding a technical co-founder won’t work. You want a unicorn to passionately trust YOUR idea after 2 meetings. Why should THEY risk money for YOUR idea? You’re the money guy. RAISE the money for YOUR idea! How passionate are you about software? And you want to build one now? This is a subtle vulnerability. ChatGPT hallucinated pip install huggingface-cli. Sosomeone created the package and got 30,000 downloads! Video-Llava is a video LLM MusicCNN-embeddings provides embeddings for music genre classification How I write podcast. Paul Graham essays Write simply. It helps communicate. (Don’t concise if communication worsens.). It forces you to make the idea better Do lame stuff. Else you won’t start. Low standards drive creativity The more to delete, the better your writing. Read your piece. Highlight what feels poor. Fix it. Ask friends to highlight what’s BORING? UNCONVINCING? Delete the first, brainstorm the second. Or ask, what’s the 10% to cut and 10% to keep. Write about stuff you don’t know above the. Writing GENERATES ideas Write about what’s BUS. GENERAL and SURPRISING. (Laughter is a sign of comprehension.) Do HARD things to cultivate taste. Spend more time with people who generate ideas in you. Ravi chithappa. Ram. Ankor. Ganes. Books! Build taste. I have a taste for picking technologies. Data visualization. Retrospect. Write down what you like and dislike. Copy what you REALLY like. Guilty pleasures. A benefit of lower standards is that it let’s you pick the path less travelled. ITERATE. Discuss ideas. Iterate. Acknowledge. ITERATE.

This week, I learned: sqlite-schema-diagram generates schemas for SQLite databases using Graphviz TechEmpower web server benchmarks place Rust servers on top browser.new is a good example of a browser agent. It slowly but independently does a good job of achieving the result. Example: What crew is common in Ingrid Bergman - Cary Grant films? twinny is an open source VC Code Copilot alternative. typesense supports embeddings natively. Binary embeddings are good enough. Cohere releases binary embeddings. Extract.langchain.com is a poor early interface to featurize unstructured.io Hume.ai offers voice emotion API and emotion-based conversational responses. An empathic AI. Rust is non-trivial. Inspired by We are under DDoS attack and we do nothing, I “wrote” a small binary that serves a parquet file as JSON. It failed and I couldn’t fix it. spleeter is a better alternative to demucs. Splits audio into pyannote-audio does speaker diarization uvicorn is faster than hypercorn but hypercorn supports HTTP/2 and HTTP/3. FastAPI with uvicorn is reasonably fast. Representational engineering lets you control LLM output based on preference on the fly. When I set up a training: On inviting for DuckDB workshop on Sun evening, Gramener starts accepting immediately, Straive doesn’t. Straive has high spread of joining time. When joining Gitlab Pipelines Workshop, Straive starts meeting (e.g. Premlal) many minutes early. Gramener floods in (due to alert). Straive streams in slowly. Gitlab Pipelines Workshop acceptances: Gramener 47, Straive 100

This week, I learned: Ways to expand mental models DISCOVER mental models. Review beliefs diary. DIVERSIFY. Find INFLUENTIAL (not dull) people with different backgrounds. Experiment! New environment, approach, perspective Be open. Change your mind. APPLY. Practice regularly Ways to use inversion “Pre-mortem” is an analysis at the beginning of how a project failed. Then avoid that “Red team” or “Black hat” are designated to contradict. Having a PoV IS a hypothesis. Always having a PoV allows us to detect anomalies and learn. Control vectors in real-time lets you control response in real-time OIDC is Open ID Connect. It’s like OAuth2 but more. Azure and Google support it. Planka is an open-source Trello There is a https://myapplications.microsoft.com/ that serves as a starting point. Might be helpful Instructor lets you create structured JSON output.