Hi HN, I’m Abrar Nasir Jaffari, co-founder of HackLikeMe. We built an agentic CLI because we were tired of the context-switching between LLM web chats and the terminal when doing DevSecOps work.Most AI coding assistants are just wrappers for file editing. We’ve built 6 specialized agents (Coder, FullStack, Security, DevOps, Plan, Monitor) that have native terminal access.<p>It doesn't just suggest code; it can:<p>Run nmap to audit your local network.<p>Use tshark to analyze packet captures.<p>Manage docker containers and kubectl clusters.<p>The 'Pause to Think' feature: Before it executes a command, it generates a reasoning plan so you can see why it's about to run a specific script."<p>The "Beta" Offer: "We launched yesterday and we're currently in beta. We are giving free Pro access for the first 100 HN users—no credit card required.<p>We’re running on a mix of AWS and GCP (leveraging some credits we just landed), so we’re able to offer some decent compute for the reasoning models during the beta.
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Monitor the status of all your coding agents to understand which ones are waiting for your input. Written in rust and relies on tmux
I’ve tried most popular personal finance apps over the last few years, and I always end up quitting.<p>For me, the main reasons are:<p>- Core functionality hidden behind paywalls<p>- UX that feels bloated or optimized for upsells<p>- $100+/year pricing for some<p>- Needing multiple separate tools (budgeting, tracking, investments) with manual syncing and often no decent mobile app<p>I’m starting an open personal finance tool as a side project because I want something I’d actually stick with long-term.<p>Before locking myself into the wrong design, I’d love to hear from others:<p>- Why did you stop using finance apps (if you used any)?<p>- What features are must-haves vs. nice-to-haves?<p>- What made a tool “click” for you — or never click at all?<p>Happy to hear if this feels redundant or already solved better elsewhere.
We’ve validated the Sigma Runtime architecture (v0.4.12) on Google Gemini-3 Flash, confirming that long-horizon identity control and stability can be achieved without retraining or fine-tuning the model.<p>The system maintains two distinct personas (“Fujiwara”, a stoic Edo-period ronin, and “James”, a formal British analyst) across 220 dialogue turns in stable equilibrium.
This shows that cognitive coherence and tone consistency can be controlled at runtime rather than in model weights.<p>Unlike LangChain or RAG frameworks that orchestrate prompts, Sigma Runtime treats the model as a dynamic field with measurable drift and equilibrium parameters.
It applies real-time feedback — injecting entropy or coherence corrections when needed — to maintain identity and prevent both drift and crystallization.
The effect is similar to RLHF-style fine-tuning, but done externally and vendor-agnostic.<p>This decouples application logic from any specific LLM provider.
The same runtime behavior has been validated on GPT-5.2 and Gemini-3, with Claude tests planned next.<p>We use narrative identities like “Fujiwara” or “James” because their linguistic styles make stability easy to verify by eye.
If the runtime can hold these for 100+ turns, it can maintain any structured identity or agent tone.<p>Runtime versions ≥ v0.4 are proprietary,
but the architecture is open under the Sigma Runtime Standard (SRS):
<a href="https://github.com/sigmastratum/documentation/tree/main/srs" rel="nofollow">https://github.com/sigmastratum/documentation/tree/main/srs</a><p>A reproducible early version (SR-EI-037) is available here:
<a href="https://github.com/sigmastratum/documentation/tree/bf473712ada5a9204a65434e46860b03d5fbf8fe/sigma-runtime/SR-EI-037/code" rel="nofollow">https://github.com/sigmastratum/documentation/tree/bf473712a...</a><p>Regulated under DOI: 10.5281/zenodo.18085782 —
non-commercial implementations are fully open.<p>HN discussion focus:
– Runtime-level vs weight-level control
– Model-agnostic identity stability
– Feedback-based anti-crystallization
– Can cognitive coherence be standardized?