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Most AI assistants forget everything after each session. Memoria remembers, forgets, and evolves—extracting personal facts, resolving contradictions, and reflecting on what it knows. This post shares the journey of building a production‑ready MemoryAgent for the Qwen Cloud Hackathon, Track 1 . Inspiration Every conversation with a typical chatbot starts from zero. You tell it you're allergic to peanuts on Monday, and by Wednesday it recommends pad thai with crushed peanuts. The model doesn't forget; it never had long‑term memory in the first place. Without durable knowledge about who you are, real personalisation is impossible. We built Memoria to solve that problem: a personal AI with human‑like memory that remembers what matters, forgets what fades, resolves contradictions, and evolves its understanding of you over time. Real memory isn't a bigger context window—it's extraction, prioritisation, decay, consolidation, and reflection. The hackathon challenged us to deliver a memory‑efficient, production‑grade MemoryAgent, and we built one from the ground up on Alibaba Cloud. What Memoria does Memoria organises knowledge in three deliberate tiers: Session Memory (Redis) – the last 10 messages of the active chat. Personal Memory (PostgreSQL 16 + pgvector) – user‑centric facts embedded with text-embedding-v3 , ranked by hybrid scoring, and subject to decay, consolidation, and conflict resolution. Context Archive – full transcripts stored for on‑demand search, never polluting routine retrieval. Other key features: Autonomous memory lifecycle : daily decay, weekly consolidation, and background reflection. Personal Intelligence toggle : global memory access vs. session‑only. Memory‑Less incognito mode : no memory reads or writes. MCP skills server : exposes get_core_memories , get_user_preferences , forget_memory , and strengthen_memory to any Qwen agent. Conflict detection & versioning : contradictory facts are automatically flagged and superseded. Persona customisation :

If your team has ever spent 20 minutes on a pull request arguing about brace placement instead of the actual bug, welcome to the club. StyleCop was the tool to keep C# codebases from turning into everyone's personal-style soup. It did its job. But it's also old, slow, and wasn't built for a world where half your codebase might be vibe coded. The good news: .NET now ships with everything you need to enforce style natively, faster, and without an extra NuGet package. This post walks through why the old approach creaks under modern workloads, and gives you a copy-pasteable migration plan to fix it. Table of Contents The Real Cost of Style Debates Quick Refresher: What Is StyleCop? Do We Even Need Linters Now That AI Writes Code? The Problem With StyleCop: It's Slow The Modern Toolbox The 3 Layer Guardrail Strategy Step by Step Migration Guide Before and After Seeing It In Action Key Takeaways Wrapping Up The Real Cost of Style Debates Nobody has ever gotten a promotion for winning a tabs-vs-spaces argument. Yet these debates eat real time: PR bikeshedding ,reviewers nitpick spacing instead of catching actual logic bugs. Noisy git diffs ,one developer's auto-formatter touches 300 lines to fix a 3-line bug. Cognitive overhead ,jumping between services that each "feel" different slows everyone down. A linter's whole job is to make these arguments boring and automatic, so humans can focus on things that actually matter,like whether the code works . Quick Refresher: What Is StyleCop? StyleCop (and its Roslyn-based version, StyleCop.Analyzers ) is a static analysis tool that checks the visual grammar of your C# code,not bugs, not security holes, just style: Are public members documented? Are namespaces organized consistently? Do braces follow the "approved" pattern? It's not checking if your code is correct . It's checking if your code looks correct. Do We Even Need Linters Now That AI Writes Code? Short answer: yes, more than ever. AI coding assistants (Copilot, Cursor, and

I've been building a level editor for Quake 3 that runs entirely in the browser: Radiant-style layout, brush and patch editing, CSG, terrain sculpting, and entity editing. It opens and saves .map file and you can play the maps you build directly in the browser using a webassembly build of ioquake3.

A recent road trip in an EV revealed just how much faster and more reliable DC Fast charging has become in the U.S.

Some of my kitchen appliances are LG and I installed the LG ThinQ app on my phone. Sometimes I like to leave a cold dish in the oven before I go out then remotely start it when I’m on my way back home, so I arrive to a nice hot dinner. After a recent HN post about LG malware and further research, I decided to kick my appliances out of the network. I also tried to remove my user account just to find out that in order to use the app I need to first accept the new terms of use. I asked ChatGPT for

TL;DR: Turn hours of raw GoPro footage + a .fit file into a 60-second highlight reel with ride telemetry burned in. Every second of the ride is scanned by gemini-3.5-flash, as is the clip ranking + curation. The whole thing costs about $0.04 per ride and takes 10 minutes. Longer version: Road cycling has been my primary form of exercise, social outlet, therapy, wardrobe expense, and personality trait for almost a decade. I ride most weekends, usually out of Manhattan and up 9W. By the end of a r

This is a submission for DEV's Summer Bug Smash: Clear the Lineup . Project Overview Formbricks is an open-source survey and experience-management platform. Its packages/surveys package ships the client-side survey runner, and inside it, packages/surveys/src/lib/logic.ts is the module that decides, for every question and every "skip logic" / branching rule a survey author configures, whether a given condition ( equals , doesNotEqual , contains , isEmpty , and friends) is true for a respondent's answer. This is pure, unglamorous logic — but it's load-bearing: it's what routes respondents through the correct sequence of questions. I picked up issue #8527 for the "Clear the Lineup" track, which reports that doesNotEqual conditions weren't behaving correctly for single-choice questions. Bug Fix or Performance Improvement The doesNotEqual operator is supposed to be the exact logical negation of equals . For most answer shapes it was. But for MultipleChoiceSingle answers, the survey runtime sometimes represents the selected value as a single-element array rather than a bare string (this happens via getLeftOperandValue , e.g. when a choice answer is merged with an "other" option path). To handle that shape, both equals and doesNotEqual had a special-case clause that unwraps a one-element array and compares its only entry to the right-hand value. equals 's fallback: return ( ( Array . isArray ( leftValue ) && leftValue . length === 1 && typeof rightValue === " string " && leftValue . includes ( rightValue )) || leftValue === rightValue ); doesNotEqual 's fallback (before the fix): return ( ( Array . isArray ( leftValue ) && leftValue . length === 1 && typeof rightValue === " string " && ! leftValue . includes ( rightValue )) || leftValue !== rightValue ); At a glance this looks like a faithful "negate every sub-expression" mirror of equals . It isn't. The array-includes clause was correctly inverted ( !leftValue.includes(rightValue) ), but the second disjunct, leftValue !==

This is a submission for DEV's Summer Bug Smash: Clear the Lineup . Project Overview Typst is the Rust-based markup typesetting system that's been quietly eating LaTeX's lunch for the last couple of years — you write plain-text markup, Typst compiles it to PDF (or HTML) with a real incremental compiler underneath instead of a multi-pass macro soup. Part of what makes it pleasant is that it exposes its own evaluator to itself: a document can call #eval("some typst code") and get back whatever that code produces, at runtime, inside the document being compiled. It's a small feature, but it opens up a surprisingly deep rabbit hole, which is exactly where this bug was hiding. Bug Fix or Performance Improvement The problem: typst/typst#8632 reports that Typst crashes instead of erroring when #eval is used to recursively re-import the file it's running in. The repro is one line. Save this as overflow.typ : #eval("import \"overflow.typ\"") Then: $ ./target/debug/typst compile overflow.typ overflow.pdf thread 'main' (750833) has overflowed its stack fatal runtime error: stack overflow, aborting No PDF, no diagnostic, no exit code you can act on — just a native stack overflow and a SIGABRT. Typst has had cyclic-import detection since forever; a normal top-level import "overflow.typ" inside itself gets caught cleanly with an error: cyclic import message. Something about routing the import through #eval specifically was bypassing that check. The hunt I'll be upfront about how this investigation actually went: I worked through it with Claude (Anthropic's coding agent) rather than solo. The triage pass it did before I sat down with the code had already narrowed the search to one function — eval_string in crates/typst-eval/src/lib.rs — and named the suspect line. That's a big head start, but a named suspect isn't a confirmed root cause, so the actual work was reading that function next to its sibling, the file-level eval() a few lines above it, and checking the claim against the s