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Are AI Agent Engrams Open Source or Proprietary? The short answer: both, and the split matters. The major agent-memory engines — Mem0, Letta, Cognee, Graphiti, LangMem, and PLUR — are all Apache-2.0 or MIT licensed on GitHub. But "open source" and "open format" are not the same thing. A project can ship under Apache-2.0 while storing your memories in opaque vector blobs you cannot read, edit, or export. The real question is not whether the software is open — it usually is — but whether your memories are. This distinction separates the field into three tiers: fully open (software + format + data you own), open-core (software is open, but the hosted memory is not portable), and fully proprietary (memory baked into a model provider's infrastructure, no export at all). Why this question exists AI agents face a brutal constraint: they forget. Every new session starts blank. Every context window overflows. The fix is persistent memory — but persistent where, and in whose format? The term "engram" comes from neuroscience. Richard Semon coined it in 1904 for the physical trace a memory leaves in biological tissue (Semon, 1904; cited in Wikipedia, "Engram (neuropsychology)"). Applied to AI agents, an engram is one discrete thing an agent has learned — a correction, a preference, a procedure — stored so it survives across sessions. The question "open source or proprietary?" asks two things at once: Is the engine that stores and retrieves engrams open source? Is the format those engrams are stored in open — readable, editable, portable? Conflating the two is how vendors end up with open-source repos and locked-in data. Tier 1: Fully open — software, format, and data These projects ship under permissive licenses (Apache-2.0 or MIT) AND store memory in a format you can inspect, edit, and export. Project License Stars (Jul 2026) Memory format Data ownership PLUR Apache-2.0 ~215 Human-readable YAML engrams Yours — plain files PLUR stores each engram as a plain-text YAML entry — an

Scan a menu, pay a bill, onboard a smart plug, and you are using a piece of technology that was never meant for any of those things. The QR code was invented in 1994, and its original job was tracking car parts on a factory floor. Understanding why it was built the way it was explains why it now shows up on nearly every connected device. Who invented the QR code The QR code was created in 1994 by a team led by engineer Masahiro Hara at Denso Wave, a subsidiary of the Toyota group in Japan. At the time, the automotive industry ran on standard one-dimensional barcodes, the striped labels still seen on grocery products. Those barcodes held very little data, usually around 20 characters, and a busy assembly line often needed a worker to scan ten different labels on a single box of components. It was slow, and it was error-prone. Hara wanted a code that could store far more information and be read much faster. His answer was to go two-dimensional: instead of encoding data only in the width of vertical bars, a QR code (short for "Quick Response") stores data across a grid of black and white squares, packing in thousands of characters in a fraction of the footprint. The board game that shaped it The most famous detail of the QR code's origin is where Hara found his design inspiration. He was reportedly playing the board game Go on a lunch break, staring at the black and white stones arranged on the grid, when the idea of encoding information in a two-dimensional matrix of light and dark cells clicked into place. The harder problem was speed. A scanner needs to find the code and figure out its orientation before it can read anything, and in a factory a label might be rotated any which way. Hara's team solved this with the three distinctive square markers in the corners of every QR code. Those "position detection patterns" use a ratio of black to white areas that almost never occurs by chance in printed material, so a scanner can instantly locate the code and work out its an

Web Accessibility for Startups: 5 Small Wins That Scale Palak jain Palak jain Palak jain Follow Oct 7 '25 Web Accessibility for Startups: 5 Small Wins That Scale # webaccessibility # webdev # frontend # startup 21 reactions 4 comments 4 min read

Just why? ʅ͡͡͡͡͡͡͡͡͡͡͡(̸̢̛̼̞̭͋ͅ)̸͚̰͛̔̾̀̿͒͂:̴͓̞̑̌̂̆̊͋̀:̸͎̟̯̂̓̌ ҉ ͡ ͞ ͞ ͞ ҉● ࿀ ● ࿀ ● ҉⃝ ⃝͢ ͞ ͘ ͞⃝̕ ͢ ̛ ⃝ ̸ ̡ ͢⃝̧ ͡ ͡ ̀ ̧ ̢⃝͜ ҉ ͞ ͞ ⃝͞ ͞ ͡⃝ ⃝҉҈҉҈҉҈҉҈҉҈҉ :̶̢͙͙͕̠̩͆(̷̮͍͚̫͚͂̍)̵̳̗̊( ̟̞̝̜̙̘̗̖҉̵̴̨̧̢̡̼̻̺̹̳̲̱̰̯̮̭̬̫̪̩̦̥ What am I supposed to do with this? It's un-Googleable. Unpronounceable. It doesn't even render the same way on various platforms. It looks one way on YouTube Music. Another on Apple Music. And yet another on Bandcamp. Even the artist name ⣎⡇ꉺლ༽இ•̛)ྀ◞ […]

Hi, I'm Cesar, a Senior Software Engineer with 10+ years of experience. In 2023, I got sick and...

GenPage is a generative AI system developed by Netflix to replace its traditional multi-stage recommendation pipeline by directly generating personalized user homepages. GenPage leverages user history and request context as a prompt to produce the entire page, resulting in improved user engagement and reduced serving latency. By Sergio De Simone

Following the success of its $99 Kodak-branded Snapic A1, Reto Project is releasing the Kodak EC35, an even more affordable 35mm film camera for just $34.99. The EC35 certainly isn't fancy. Its 25mm acrylic lens with a fixed f/10 aperture and 1/100 shutter speed basically put it on par with a drugstore disposable. The fact […]

On the latest episode of Equity, we debate whether Apple's lawsuit will cast over OpenAi's much-discussed plans to get into hardware and go public.

When a new video shows up in one of our regional crawlers, three things need to happen almost immediately: the SQLite FTS5 search index for that region needs a new row, the discovery ranking cache needs to be invalidated, and the sitemap generator needs to know a URL was born. For a long time we did all of this inline, inside the same cron process that fetched the video. It worked until it didn't. A slow FTS5 rebuild would stall the fetch loop, a sitemap write would fail silently, and a crash halfway through meant one region had the video indexed and another didn't. The fetch and the fan-out were fused together, and every failure was a partial failure. The fix was to stop treating "a video was discovered" as a function call and start treating it as an event. At TrendVidStream we run discovery across 8 regions, and the moment we introduced NATS JetStream as the spine between the crawler and the downstream consumers, the whole system got calmer. This post is the concrete version of how we did it: the stream config, the publishers, the consumers, and the mistakes we made that you can skip. Why Not Just Use a Queue Table in SQLite We already had SQLite everywhere, so the obvious move was a jobs table. We tried it. The problems showed up fast: Polling latency vs. load tradeoff. Poll every second and you hammer the DB with mostly-empty SELECT queries across 8 regions. Poll every 30 seconds and your search index lags noticeably behind your crawler. No fan-out. One row, one worker. If the sitemap generator and the FTS5 indexer both need the same event, you either duplicate rows or invent a consumed_by bitmask. Both are ugly. Locking. SQLite's writer lock means the queue table and the actual data table start contending under the multi-region cron bursts we run. Cross-region delivery. Our regions aren't all on the same box. A queue table doesn't cross machines without you building a replication story on top. JetStream solves all four: push-based delivery (no polling), multipl