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I recently stumbled across an interesting concept while browsing some indie storytelling projects online. It’s essentially a platform experimenting with context aware fiction short stories that subtly incorporate elements from the reader’s environment (time, device behavior, familiar digital patterns like messaging timestamps, etc.) to make the narrative feel more immersive.

What caught my attention wasn’t the story itself but the design idea behind it. Instead of building a full ARG or complex interactive game, the approach seems to rely on small contextual cues that readers already trust: things like system time, common app interfaces, and familiar UI behaviors. When those are blended into the narrative structure, it creates a strange effect where the reader briefly questions whether something is part of the story or just happening around them.

From a product/design perspective it feels like a lightweight way to experiment with interactive fiction without heavy infrastructure. You don’t need complex branching paths or game engines just clever writing paired with subtle contextual triggers.

The project I ran into was called pokostories, which seems to be exploring this idea with serialized short fiction.

Curious if anyone here has worked on similar approaches to narrative experiences on the web especially using simple browser or UI context to enhance immersion.

Comments URL: https://news.ycombinator.com/item?id=47327522

Points: 1

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Article URL: https://twitter.com/shahinlooo/status/2031362334174703657

Comments URL: https://news.ycombinator.com/item?id=47327520

Points: 1

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Article URL: https://chenyu-li.info/blog/

Comments URL: https://news.ycombinator.com/item?id=47327506

Points: 1

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I built this after reading “The Tail End” by Tim Urban. I wanted a simple way to understand my lifespan relative to the people most important to me.

SandsOfTi.me helps you visualize your approximate lifespan, healthy years remaining, and the time you likely left with your parents, children, spouse, and more.

Stuff of note:

- Single-page app (HTML/JS)

- No sign-up required

- Nothing you input is stored

- Save your visualization as an image, or export your data so you can restore it later

- All lifespan estimates are based on U.S. Social Security Administration Cohort Life Tables (already considering other data sources, but using more than one standard gets complex)

Feedback and suggestions are very welcome!

Related resources:

SSA Cohort Life Tables - https://www.ssa.gov/oact/HistEst/CohLifeTables/2024/CohLifeT...

The Tail End, by Tim Urban @ Wait But Why: https://waitbutwhy.com/2015/12/the-tail-end.html

“When This Number Hits 5200 - You Will be Dead”, by Kurzgesagt: https://www.youtube.com/watch?v=JXeJANDKwDc

Comments URL: https://news.ycombinator.com/item?id=47327482

Points: 1

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Article URL: https://straitsweeper.com/

Comments URL: https://news.ycombinator.com/item?id=47327479

Points: 1

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Article URL: https://github.com/torlando-tech/columba

Comments URL: https://news.ycombinator.com/item?id=47327473

Points: 1

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Article URL: https://blog.krauq.com/post/ctf-is-dying-because-of-ai

Comments URL: https://news.ycombinator.com/item?id=47327454

Points: 1

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Article URL: https://iai.tv/articles/reality-is-not-a-controlled-hallucination-auid-3517

Comments URL: https://news.ycombinator.com/item?id=47327444

Points: 1

# Comments: 0

Hey HN, I'm Mario. I recently talked to a colleague about AI, agents and how software development will change in the future. We were wondering why we should even share code anymore when AI agents are already really good at implementing software, just through prompts. Why can't everyone get customized software with prompts?

"Share the prompt, not the code."

Well, I thought, great idea, let's do that. That's why I built Open Prompt Hub: https://openprompthub.io.

Think GitHub just for prompts.

The idea is simple: Users can upload prompts that can then be used by you and your AI tools to generate a script, app, or web service (or prime their agent for a certain task): Just past it into your agent or ide and watch it build for you. If the prompt does not 100% covers your usecase, fork it, tweak it, et voila: tailor-made software ready to use!

The prompts are simple markdown files with a frontematter block for meta information. (The spec can be found here: https://openprompthub.io/docs) They versioned, have information on which AI models build it successfuly and have instructions on how the AI agent can test the resulting software.

Users can mention with which models they have successfully or unsuccessfully executed a prompt (builds or fail). This helps in assessing whether a prompt provides reliable output or not.

Want to create a open prompt file? Here is the prompt for it which will guide you through: https://openprompthub.io/open-prompt-hub/create-open-prompt

Security! Always a topic when dealing with AI and prompts? I've added several security checks that look at every prompt for injections and malicious behavior. Statistical analysis as well as two checks against LLMs for behaviour classification and prompt injection detection.

It's an MVP for now. But all the mentioned features are already included.

If this sounds good, let me know. Try a prompt, fork it, or tell me what you'd change in the spec or security scanner. I'm really curious about what would make you trust and reuse prompts. Or if you like the general idea...

Comments URL: https://news.ycombinator.com/item?id=47327438

Points: 1

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Article URL: https://railsatscale.com/2026-03-09-faster-bundler/

Comments URL: https://news.ycombinator.com/item?id=47327428

Points: 1

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Article URL: https://vincentcarchidi.substack.com/p/the-obscure-relation-of-appropriateness

Comments URL: https://news.ycombinator.com/item?id=47327408

Points: 1

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Article URL: https://hyperfluid.page

Comments URL: https://news.ycombinator.com/item?id=47327401

Points: 1

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Article URL: https://buttondown.com/hillelwayne/archive/llms-are-bad-at-vibing-specifications/

Comments URL: https://news.ycombinator.com/item?id=47327387

Points: 2

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Article URL: https://bloomberg.github.io/js-blog/post/standardizing-source-maps/

Comments URL: https://news.ycombinator.com/item?id=47327383

Points: 2

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Article URL: https://www.washingtonpost.com/politics/2026/03/10/social-security-data-breach-doge/

Comments URL: https://news.ycombinator.com/item?id=47327367

Points: 9

# Comments: 2

AI agents are shipping fast (LangChain, CrewAI, MCP servers, OpenAI Agents SDK) but there's no unified way to secure and govern them. We built g0 to be that control layer.

What g0 does across the agent lifecycle: g0 scan - Static + behavioral analysis of agent code. 1,180 rules across 12 security domains, 10 frameworks (LangChain, CrewAI, MCP, OpenAI, Vercel AI, Bedrock, AutoGen, LangChain4j, Spring AI, Go AI), 5 languages. Detects toxic tool chains, taint flows, overprivileged descriptions, missing sandboxing. Integrated threat intelligence checks tool URLs and dependencies against 55+ IOCs and known CVEs. g0 test - Dynamic adversarial red teaming. Fires prompt injections, data exfiltration attempts, tool abuse sequences, jailbreaks, and goal hijacking payloads at your running agents. 3-level progressive judge (deterministic, heuristic, LLM). Works over HTTP and MCP. g0 endpoint - Discovers every AI tool on the machine (Claude Code, Cursor, Windsurf, Zed, 15+ tools), inventories MCP servers, and surfaces misconfigurations. Think nmap but for your AI developer surface. g0 daemon - Continuous runtime monitoring. Behavioral baselines with anomaly detection, cost circuit breaker, correlation engine linking events across sources into attack chains, and a kill switch for when things go sideways. g0 detect - MDM enrollment detection (Jamf, Intune, Mosyle, Kandji, etc.), running AI agent discovery, and host hardening audit in one view. First-class OpenClaw support. g0 is the only security tool that understands OpenClaw's architecture: gateway hardening (18 probes), SKILL.md/SOUL.md/MEMORY.md analysis, cognitive drift monitoring via SHA-256 baselines, deployment audits, config hardening, and ClawSec CVE feed integration. If you're running OpenClaw in production, g0 catches what generic scanners miss. Compliance built in, not bolted on. Every finding maps to 10 standards: OWASP Agentic Top 10, OWASP LLM Top 10, NIST AI RMF, ISO 42001, EU AI Act, MITRE ATLAS, and more. Generate evidence records, compliance reports, and enforce policies via .g0-policy.yaml with CI gate support. Outputs: Terminal, JSON, SARIF 2.1.0, HTML, CycloneDX AI-BOM, Markdown. Plugs into GitHub Actions, GitLab CI, or any pipeline. One command to start: npx @guard0/g0 scan . GitHub: https://github.com/guard0-ai/g0 We think the AI agent ecosystem needs the same security tooling maturity that web apps got with Burp Suite and Semgrep, but purpose-built for agents. Happy to answer questions about the architecture or threat model.

Comments URL: https://news.ycombinator.com/item?id=47327358

Points: 1

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Article URL: https://www.tomsguide.com/computing/virtual-reality/nvidia-is-bringing-a-killer-app-to-the-apple-vision-pro-and-its-good-news-for-sim-racers

Comments URL: https://news.ycombinator.com/item?id=47327357

Points: 3

# Comments: 0

Hello HN, we're Jeet and Husain from Modulus (https://modulus.so) - a desktop app that lets you run multiple coding agents with shared project memory. We built it to solve two problems we kept running into:

- Cross-repo context is broken. When working across multiple repositories, agents don't understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo.

- Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again.

Modulus shares memory across agents and repositories so they can understand your entire system.

It's an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents.

Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP. This later became Modulus.

Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context.

If you wanna see a quick demo before trying out, here is our launch post - https://x.com/subhajitsh/status/2024202076293841208

We'd greatly appreciate any feedback you have and hope you get the chance to try out Modulus.

Comments URL: https://news.ycombinator.com/item?id=47327351

Points: 1

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Article URL: https://www.thedrive.com/news/the-fat-ice-race-is-a-reminder-that-all-cars-are-meant-to-be-driven

Comments URL: https://news.ycombinator.com/item?id=47327341

Points: 1

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Article URL: https://www.parsonsmatt.org/2026/03/10/teaching_claude_to_be_lazy.html

Comments URL: https://news.ycombinator.com/item?id=47327334

Points: 1

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