Posts — Table Of Contents

lala-hakobyan/front-end-debugging-handbook

A comprehensive guide to debugging tools for modern, complex front-end applications. This handbook provides practical, real-world guidance on a complete suite of tools, from Chrome DevTools and framework extensions to integrated IDE debuggers.

DefensiveComputingChecklist.com Table of Contents

Full Table of contents for DefensiveComputingChecklist.com

Mem0: Technical Analysis Report | Notion

Table of Contents

Fix your tools

Last week I had to diagnose a bug in an open source library I maintain. The issue was gnarly enough that I couldn’t find it right away, but then I thought: if I set a breakpoint here and fire up the debugger, I will likely find the root cause very soon… and then proceed to mercilessly destroy it! So I rolled up my sleeves, set the breakpoint, fired up the debugger, and… saw the program run to completion without interruptions whatsoever.

kyju.org

Horribly misusing Rust features to provide provable memory safety and tracing garbage collection for pointer soup.

WiCER: Wiki-memory Compile, Evaluate, Refine Iterative Knowledge Compilation for LLM Wiki Systems

The LLM Wiki pattern, to compile and provide domain knowledge into a persistent artifact and serve it to LLMs via KV cache inference, promises context access at sub-second latency with zero retrieval failure. Realizing this requires solving the compilation gap: LLM compilation distilling raw documents into a wiki without catastrophically discarding critical facts. We characterize this gap across 17 RepLiQA domains (6,800 questions): we observe that full context KV cache inference outperforms RAG on curated knowledge (4.38 vs. 4.08 out of 5, 7.3 faster TTFT) but degrades below RAG at scale due to attention dilution, and blind compilation fails entirely (2.14 to 2.32 vs. 3.46, 53 to 60% catastrophic failure rate). To address the compilation gap, we propose WiCER (Wiki-memory Compile, Evaluate, Refine), an iterative algorithm inspired by counterexample-guided abstraction refinement (CEGAR) that closes this gap. WiCER evaluates compiled wikis against diagnostic probes, identifies dropped facts, and forces their preservation in subsequent compilations. One to two iterations recover 80% of lost quality (mean 3.24 vs. 3.47 for raw full-context across the 15 topics with baselines), reducing catastrophic failures by 55% relative. An ablation across all 17 topics confirms that targeted diagnosis (+0.95), not generic pinning (+0.16), drives the gains. All code and benchmarks are released for reproducible research.

Decoded: GNU coreutils – MaiZure's Projects

Taking apart the GNU coreutils

What I Found Interesting in Claude Code's Source

A breakdown of the most interesting engineering patterns in Claude Code's leaked source code: composable system prompts, runtime instruction injection, context compression, forking, prompt caching architecture, and more.

Microsoft Struggling With Hundreds of AI-Discovered Security Bugs — ProPublica

Anthropic’s Mythos has flagged bugs faster than Microsoft can fix them. Documents reviewed by ProPublica reveal the tech giant's “mad dash” behind the scenes to patch holes before hackers can find and exploit them.

compilers & resumania · oh hey, it's owais

Dev Log 54: 2026-06-10

Deep-Research Agents Can Be Poisoned via User-Generated Content

Deep-research agents, i.e., systems that rely on multi-agent pipelines to iteratively retrieve, synthesize, and cite Web content in order to produce structured reports, are rapidly replacing traditional search for both routine and complex information needs. These agents issue many related queries during a single research session. We show that for many common search topics, they repeatedly retrieve the same user-generated content (UGC) pages from platforms such as Reddit and Wikipedia. Next, we argue that this retrieval overlap creates a concentrated attack surface: an adversary who appends a short, crafted text to a single, frequently retrieved UGC page can cause the agent to cite attacker-chosen content and promote attacker-chosen entities across many related queries. We evaluate this attack on three representative deep-research systems (STORM, Co-STORM, and OmniThink) across multiple query clusters. We also study defenses at different stages of the pipeline, including source-level filtering and output-based detection. Our findings highlight a fundamental vulnerability in how deep-research agents retrieve and integrate web content.

argv*mory

it was a not really sunny day in tpa, fl, when i got the message from cpp swift's president. it was an invite to help with their info sec conference, the Tech Symposium. they were looking for community members who would be willing to help with different aspects, tabling, ctf challenges, organization. my brain started to turn and i knew i wanted to do something really funny. last year in october, i had tabled at the CSUF's OSScon, ran by their security club, OSS. the tabling was fun and i got to present some malware that i was working on for a research project with mitre , which is avalible here. it was cool to get people to mess around with malware when they've never seen it before, but for the tech Symposium, i had a more substantial idea of what i wanted to do.

Dremel: interactive analysis of web-scale datasets: Proceedings of the VLDB Endowment: Vol 3, No 1-2

Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system ...

hunk — review-first terminal diff viewer

Hunk is a review-first terminal diff viewer for agent-authored changesets. Multi-file review stream, inline AI annotations, watch mode, and Git/Jujutsu integration.