NewsWorld
PredictionsDigestsScorecardTimelinesArticles
NewsWorld
HomePredictionsDigestsScorecardTimelinesArticlesWorldTechnologyPoliticsBusiness
AI-powered predictive news aggregation© 2026 NewsWorld. All rights reserved.
Trending
FebruaryTimelineMarketsDigestTalksIranFaceDiplomaticThursdayStrikesTargetsStatePredictionLaunchesMilitaryPressureNuclearIranianIsraelIssuesChinaHongParticularlyGovernment
FebruaryTimelineMarketsDigestTalksIranFaceDiplomaticThursdayStrikesTargetsStatePredictionLaunchesMilitaryPressureNuclearIranianIsraelIssuesChinaHongParticularlyGovernment
All Articles
Self-improving software won't produce Skynet
Hacker News
Published about 5 hours ago

Self-improving software won't produce Skynet

Hacker News · Feb 26, 2026 · Collected from RSS

Summary

Article URL: https://contalign.jefflunt.com/self-improving-software/ Comments URL: https://news.ycombinator.com/item?id=47161498 Points: 12 # Comments: 2

Full Article

Self-Improving Software: The Cycle of Improvement In the traditional software development lifecycle, there is often a widening gap between the code we write and the documentation that describes it. We build features, fix bugs, and refactor architectures, but the READMEs, design documents, and internal wikis frequently lag behind. This "documentation debt" becomes a significant hurdle for both human developers and the AI agents we collaborate with. However, as AI becomes more agentic, we are entering a new era where software can, in a very real sense, become self-improving. The Cycle of Improvement Agentic AI possesses a dual capability that fundamentally changes how we maintain software: Deep Understanding: It can read and synthesize existing project documentation, codebases, and historical context to understand the why behind the current state. Autonomous Updating: It can automatically update that same documentation based on the recent code changes it has just authored. This creates a continuous feedback loop. When an AI agent implements a new feature, its final task isn't just to "commit the code." Instead, as part of the Continuous Alignment process, the agent's final step is to reflect on what changed and update the project's knowledge base accordingly. In this model, the documentation isn't a static artifact; it's a living part of the system that evolves alongside the code. The software "improves" itself by ensuring its own internal representation and external documentation are always accurate, making the next iteration even more efficient. The Reality of "Self-Improving" When we hear the term "self-improving software," our minds often jump straight to science fiction. We envision runaway artificial intelligences like Skynet from Terminator or the Master Control Program (MCP) from TRON—entities that develop their own agendas and grow beyond human control. But it's time for a reality check: the type of self-improvement we’re talking about is far more pragmatic and much less dangerous. The AI is acting at your direction and following your lead. While it is autonomous in its execution of tasks, it is unlikely to go rogue. It doesn't possess a sense of self-will, self-determination, or a secret plan to take over the world. It is a highly sophisticated tool designed to automate the exact same iterative processes that human developers already use. We have always strived to continuously improve and document our systems. We’ve used CI/CD pipelines to automate testing and deployment. Self-improving software is simply the next logical step: the automation of knowledge maintenance. Tightening the Feedback Loop By having the AI write and maintain its own documentation, we dramatically tighten the feedback loop. When you start a new task with an agent, it doesn't have to guess how a complex module works based on a year-old README. It can rely on documentation that was updated just hours ago by the previous agent (or even itself). This reduces the "onboarding time" for every new subagent and minimizes the risk of hallucinations caused by stale information. This self-documentation a key facet of Continuous Alignment - to keep the AI in sync with our own designs and the direction in which we want our systems to evolve. It ensures that the shared understanding between human and AI is always grounded in the most recent reality of the codebase. Looking Ahead Self-improving software isn't about creating a digital god; it's about building a more resilient, maintainable, and understandable system. By closing the loop between code and documentation, we set the stage for even more complex collaborations. In the next part of this series, we’ll explore how these same agentic capabilities can be applied to one of the most challenging areas of software engineering: working with legacy codebases. How can an agent help us reclaim a system that has years of technical debt and missing documentation? Stay tuned. Subscribe to Continuous Alignment Get new posts delivered straight to your inbox. No spam, just essays on using AI to accomplish more. check_circle Thanks for subscribing! You've been added to the list.


Share this story

Read Original at Hacker News

Related Articles

Hacker Newsabout 4 hours ago
I don't know how you get here from "predict the next word."

Article URL: https://www.grumpy-economist.com/p/refine Comments URL: https://news.ycombinator.com/item?id=47162059 Points: 7 # Comments: 3

Hacker Newsabout 4 hours ago
Apple Needs to Copy Samsung's New Security Smartphone Screen ASAP

Article URL: https://www.wsj.com/tech/personal-tech/samsung-galaxy-s26-privacy-display-d5bce9ab Comments URL: https://news.ycombinator.com/item?id=47162002 Points: 10 # Comments: 4

Hacker Newsabout 6 hours ago
Show HN: OpenSwarm – Multi‑Agent Claude CLI Orchestrator for Linear/GitHub

I built OpenSwarm because I wanted an autonomous “AI dev team” that can actually plug into my real workflow instead of running toy tasks. OpenSwarm orchestrates multiple Claude Code CLI instances as agents to work on real Linear issues. It: • pulls issues from Linear and runs a Worker/Reviewer/Test/Documenter pipeline • uses LanceDB + multilingual-e5 embeddings for long‑term memory and context reuse • builds a simple code knowledge graph for impact analysis • exposes everything through a Discord bot (status, dispatch, scheduling, logs) • can auto‑iterate on existing PRs and monitor long‑running jobs Right now it’s powering my own solo dev workflow (trading infra, LLM tools, other projects). It’s still early, so there are rough edges and a lot of TODOs around safety, scaling, and better task decomposition. I’d love feedback on: • what feels missing for this to be useful to other teams • failure modes you’d be worried about in autonomous code agents • ideas for better memory/knowledge graph use in real‑world repos Repo: https://github.com/Intrect-io/OpenSwarm Happy to answer questions and hear brutal feedback. Comments URL: https://news.ycombinator.com/item?id=47160980 Points: 8 # Comments: 0

Hacker Newsabout 7 hours ago
Jane Street Hit with Terra $40B Insider Trading Suit

Article URL: https://www.disruptionbanking.com/2026/02/24/jane-street-hit-with-terra-40b-insider-trading-suit/ Comments URL: https://news.ycombinator.com/item?id=47160613 Points: 10 # Comments: 0

Hacker Newsabout 7 hours ago
Show HN: ZSE – Open-source LLM inference engine with 3.9s cold starts

I've been building ZSE (Z Server Engine) for the past few weeks — an open-source LLM inference engine focused on two things nobody has fully solved together: memory efficiency and fast cold starts. The problem I was trying to solve: Running a 32B model normally requires ~64 GB VRAM. Most developers don't have that. And even when quantization helps with memory, cold starts with bitsandbytes NF4 take 2+ minutes on first load and 45–120 seconds on warm restarts — which kills serverless and autoscaling use cases. What ZSE does differently: Fits 32B in 19.3 GB VRAM (70% reduction vs FP16) — runs on a single A100-40GB Fits 7B in 5.2 GB VRAM (63% reduction) — runs on consumer GPUs Native .zse pre-quantized format with memory-mapped weights: 3.9s cold start for 7B, 21.4s for 32B — vs 45s and 120s with bitsandbytes, ~30s for vLLM All benchmarks verified on Modal A100-80GB (Feb 2026) It ships with: OpenAI-compatible API server (drop-in replacement) Interactive CLI (zse serve, zse chat, zse convert, zse hardware) Web dashboard with real-time GPU monitoring Continuous batching (3.45× throughput) GGUF support via llama.cpp CPU fallback — works without a GPU Rate limiting, audit logging, API key auth Install: ----- pip install zllm-zse zse serve Qwen/Qwen2.5-7B-Instruct For fast cold starts (one-time conversion): ----- zse convert Qwen/Qwen2.5-Coder-7B-Instruct -o qwen-7b.zse zse serve qwen-7b.zse # 3.9s every time The cold start improvement comes from the .zse format storing pre-quantized weights as memory-mapped safetensors — no quantization step at load time, no weight conversion, just mmap + GPU transfer. On NVMe SSDs this gets under 4 seconds for 7B. On spinning HDDs it'll be slower. All code is real — no mock implementations. Built at Zyora Labs. Apache 2.0. Happy to answer questions about the quantization approach, the .zse format design, or the memory efficiency techniques. Comments URL: https://news.ycombinator.com/item?id=47160526 Points: 18 # Comments: 1

Hacker Newsabout 8 hours ago
Tech Companies Shouldn't Be Bullied into Doing Surveillance

Article URL: https://www.eff.org/deeplinks/2026/02/tech-companies-shouldnt-be-bullied-doing-surveillance Comments URL: https://news.ycombinator.com/item?id=47160226 Points: 34 # Comments: 1