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@akshay_pachaar

  • MongoDB Vector Search Lexical Prefilters for Precise Forgiving Search

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    @akshay_pachaar

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    Learn more about #MongoDB Vector Search: fandf.co/4qyyKcb It makes sure everything we discussed is executed before the vector math, so you only run similarity scoring on relevant candidates. If you're building anything where users make typos, need location-based results, or expect your search to be both precise and forgiving at the same time, Lexical Prefilters solve it. It's part of the vectorSearch operator (inside the $search stage) in Atlas – so if you're still on knnBeta, this is what's next. Thank you MongoDB for working with me on this one.

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-03-30 07:37 UTC

  • Vector Search 10x Cheaper with Intelligent Lexical Filtering
    Vector Search 10x Cheaper with Intelligent Lexical Filtering

    By

    @akshay_pachaar

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    A simple technique can make your vector search 10x cheaper. And you probably haven't heard of it yet. Consider this: A user searches "runnng shoes" (yes, misspelled) looking for size 10, within 10 miles, under $100. Vector search runs on 500 products, then the filters apply – and only 12 match the size, location and price. That's 500 similarity calculations to surface 12 results. And if the typo didn't get caught? Those 12 might not even include what the user wanted. Standard pre-filters would return ZERO results for "runnng" – it's not an exact match. Post-filtering catches the typo semantically but wastes compute on 488 irrelevant products first. This is how search pipelines typically work. Most teams have accepted this as normal – run vector search first to get semantically relevant results, then apply filters afterward. Standard vector search does support basic pre-filters (like "price < $100" or "size = 10"), but those filters are rigid, only handling exact matches and simple comparisons. They can't handle typos, wildcards, or complex text analysis. So you're stuck: use exact-match pre-filters and get zero results for typos, or post-filter massive datasets and waste compute. What you actually need is filtering that handles precision and fuzziness together – precise enough for "size 10" and "under $100," flexible enough to match "runnng to running," and smart enough to handle complex geospatial queries like "within 10 miles." And it needs to happen before vector search runs, not after. But the bigger point is this: – Post-filter: search everything, hope for the best.
    – Pre-filter with lexical intelligence: search only what matters, get it right. Precision and semantics work better as layers than as tradeoffs. Now that you see the problem, let me show you what the fix actually looks like in practice ๐Ÿ‘‡ This is ex [Translated from EN to English]

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-03-30 07:36 UTC

  • VibeVoice GitHub Repository – Don’t Forget to Star

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    @akshay_pachaar

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    VibeVoice GitHub: github.com/microsoft/VibeVoiโ€ฆ (don't forget to star ๐ŸŒŸ)

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-03-29 13:11 UTC

  • Microsoft VibeVoice: Revolutionary Open-Source Speech AI Models

    By

    @akshay_pachaar

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    Microsoft did it again! Speech AI models have a major limitation. They slice long recordings into tiny chunks, lose track of who's speaking, and forget all context halfway through. This is exactly what Microsoft's VibeVoice solves. It's an open-source family of frontier voice AI models for both speech recognition and speech generation. Here's what it can do: > VibeVoice-ASR processes up to 60 minutes of audio in a single pass. No chunking. It outputs structured transcriptions with who spoke, when they spoke, and what they said. > You can feed it custom hotwords like names, technical jargon, or domain-specific terms. The model uses them to significantly improve accuracy on specialized content. > VibeVoice-TTS generates up to 90 minutes of multi-speaker speech with up to 4 distinct speakers. Natural turn-taking, emotional expression, all in one pass. > VibeVoice-Realtime is a 0.5B streaming TTS model with ~300ms first-audio latency. Small enough to deploy practically anywhere. All of this is powered by continuous speech tokenizers running at just 7.5 Hz. This ultra-low frame rate preserves audio quality while making long sequences computationally feasible. I have shared the link to the GitHub repo in the replies!

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-03-29 13:11 UTC

  • Claude Skills: Self-Contained Workflow Packages for Efficient AI

    By

    @akshay_pachaar

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    What are Claude Skills? ๐—–๐—Ÿ๐—”๐—จ๐——๐—˜.๐—บ๐—ฑ was never meant to hold entire workflows. But that's exactly where they end up. General rules, coding conventions, 20-step security review processes, deployment checklists. All in one file that loads into every single session, eating context even when Claude is just renaming a variable. ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ fix this by turning workflows into self-contained packages that Claude loads only when the task demands it. Here's the idea. A skill is a folder inside .๐—ฐ๐—น๐—ฎ๐˜‚๐—ฑ๐—ฒ/๐˜€๐—ธ๐—ถ๐—น๐—น๐˜€/. Each folder contains a ๐—ฆ๐—ž๐—œ๐—Ÿ๐—Ÿ.๐—บ๐—ฑ file with two things: a ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฝ๐˜๐—ถ๐—ผ๐—ป that tells Claude when to activate it, and the workflow instructions that tell Claude what to do. The description is the trigger. Claude reads all available skill descriptions, watches the conversation, and when your request matches, it pulls in that skill automatically. You don't paste the steps. You don't type a command. Claude recognizes the intent and invokes the right skill on its own. You can also trigger any skill explicitly with a slash command like /๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜†-๐—ฟ๐—ฒ๐˜ƒ๐—ถ๐—ฒ๐˜„ when you want manual control. I recorded a deep dive on skills when they were first released, and everything in it is even more relevant today. The video below walks through exactly how this works. But auto-invocation is just the surface. The real power is what skills can carry with them. Skills are full packages, not just instruction files. A ๐—ฆ๐—ž๐—œ๐—Ÿ๐—Ÿ.๐—บ๐—ฑ can reference supporting files that live right next to it using the @ symbol. A detailed security standards document. A release notes template. A compliance checklist. Whatever the workflow needs, the skill bundles it together. Inside ๐—ฆ๐—ž๐—œ๐—Ÿ๐—Ÿ.๐—บ๐—ฑ, YAML frontmatter defines the name, description, and which tools the skill is allowed to use. The ๐—ฎ๐—น๐—น๐—ผ๐˜„๐—ฒ๐—ฑ-๐˜๐—ผ๐—ผ๐—น๐˜€ field is worth paying attention to. A security review skill only needs ๐—ฅ๐—ฒ๐—ฎ๐—ฑ, ๐—š๐—ฟ๐—ฒ๐—ฝ, and ๐—š๐—น๐—ผ๐—ฏ. It has no business writing files. Restricting tool access makes the skill safer and far more predictable. Skills live at two levels. Project skills go in .๐—ฐ๐—น๐—ฎ๐˜‚๐—ฑ๐—ฒ/๐˜€๐—ธ๐—ถ๐—น๐—น๐˜€/ and get committed to git so the whole team shares them. Personal skills go in ~/.๐—ฐ๐—น๐—ฎ๐˜‚๐—ฑ๐—ฒ/๐˜€๐—ธ๐—ถ๐—น๐—น๐˜€/ and follow you across every project. A ๐—–๐—Ÿ๐—”๐—จ๐——๐—˜.๐—บ๐—ฑ with a 20-step security process baked in is dead weight in 90% of your sessions. A ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜†-๐—ฟ๐—ฒ๐˜ƒ๐—ถ๐—ฒ๐˜„ skill that activates only when security is on the table is precision. ๐—–๐—Ÿ๐—”๐—จ๐——๐—˜.๐—บ๐—ฑ tells Claude what rules to follow. Skills tell Claude what workflows to execute. The article below is a complete guide to ๐—–๐—Ÿ๐—”๐—จ๐——๐—˜.๐—บ๐—ฑ, hooks, skills, agents, and permissions, and how to set them up properly. Akshay ๐Ÿš€ (@akshay_pachaar) x.com/i/article/203496196714โ€ฆ โ€” https://nitter.net/akshay_pachaar/status/2035341800739877091#m

    โ†’ View original post on X โ€” @akshay_pachaar, 2026-03-29 10:13 UTC