mutant

How many others get this treatment without the publicity, y'all still showed your colors. Enjoy your business practices.

This feels fishy, I everyone I submitted was 100% wrong. Another harvester

1. A structured data format for representing arguments/reasoning as graphs 2. Validation rules to prevent circular reasoning and inconsistencies 3. A protocol for AI agents to exchange these structured argument graphs

Is this a reasonable description?

What dictionary was murdered to create these terms?

Sure feels like your hn title should say "UK couples" considering that's what's in your hero. Drive traffic much?

Sell the software, not the service, or sell the software as a subscription. U gonna get shut down as a service.

Bloglink is 404

All that code and not a single screenshot

Every generation is a fail

For foundational models, I feel like this is working against the model, they already are trained as experts, they need guidance, not prose about how to be an expert

# Effective steering stack: "FastAPI + SQLAlchemy + Redis" scale: "10k RPS, sub-50ms P99" deployment: "K8s, multi-region" constraints: ["async-first", "12-factor", "observability"]

# Not this python-expert: "You are an expert in advanced Python..."

# This context: "Building FastAPI backend, PostgreSQL, Redis cache, Docker deployment" constraints: "Sub-100ms response times, 10k concurrent users" preferences: "Async-first, type hints, structured logging"

I stopped telling ai how to do their jobs a long time ago, and started context management, I get crazy better results. The only time i need to bash training in is when it doesn't know an API, then I spawn a research agent to create an updated training prompt for an API, or command, then import it as needed. Keeps the primary context window cleaner for longer.

huh, i did something like this using tmux, and shared sessions, i love seeing the full interactions

this is already done with Agents, i dont understand the point?

theres no oauth here, why call it that? its a single source local ephemeral key manager

or have i missed something entirely?

i feel like this is something g that shouldn't exist. what an opsec nightmare

wrong direction?

I'm sure that's the intent, but we're back to "how to trust" crack that problem, and you have yourself a goldmine

Let's MITM sensitive data!!!

You can't see pricing without logging in? That's pretty fucking infuriating

All those installation examples and not an example showing use

What mechanism reports page view per file?

While this pitch tugs at the heartstrings, as someone in IT/engineering, I'd pump the brakes hard. Building "emotionally available AI" isn't a prompt-hacking weekend project—it's a high-stakes alignment nightmare that well-meaning devs without deep ML safety chops are likely to botch. Here's a tight technical rundown of the red flags, sans fluff:

1. *Alignment Brittleness*: No details on fine-tuning or RLHF (e.g., using datasets like those from HELM or custom therapy corpora). Relying on prompts to "prime" a base LLM (probably GPT-like) is like duct-taping a guidance system— it fails under stress. Emotional contexts amplify risks: the model could hallucinate escalatory responses (e.g., reinforcing spirals via latent biases in pre-training data), bypassing any superficial steering. Without provable techniques like constitutional AI or red-teaming for edge cases (suicidal ideation, trauma triggers), it's unaligned output waiting to happen.

2. *Inference-Time Vulnerabilities*: Prompts alone can't enforce robust safeguards. LLMs exhibit emergent behaviors in long contexts—think jailbreaks or mode collapse where the AI "remembers" and amplifies negative patterns in journaling/mood tracking. No mention of layers like chain-of-thought with safety classifiers (inspired by Anthropic/DeepMind) means potential for toxic empathy: sassy mode goes rogue, zen turns dismissive. In voice mode, real-time audio processing adds latency-induced errors, eroding that "human feel" into something unpredictably harmful.

3. *Expertise and Oversight Gaps*: This screams "enthusiast project" without creds in AI ethics/safety (e.g., from OpenAI's Superalignment teams). Privacy claims? Fine, but "secure" journaling risks data leakage via model inversion attacks if not using differential privacy. Emotional AI demands HIPAA-level rigor, not beta vibes—missteps here could cause real psych harm, like entrenching isolation over guiding to human help.

Bottom line: Clever prompts don't solve alignment; they mask it. If you're beta-testing, demand transparency on training data, safety evals, and fallback to licensed therapists. This isn't ready for 2 AM crises—it's playing therapist without the degree. Proceed with extreme caution.

Thanks for the self host option. I tried the slack example and was very impressed with results, thank you!

Why greenfield a project with no llm choice options? Folks devote copious time to hacking Claude to use more than just anthropic.

This would be great as an API or mcp

I thought this was solved with SOPS/AGE?

#1 after 1 week on GitHub with perfect producthubt reviews? How much did all that cost you?

No, but I've already put this at the top of my tinker pile. I'm sure I will soon

Huh, this is pretty dope. I tried this example https://github.com/katanemo/archgw/blob/main/demos/samples_p...

And was pleased with what I was able to do. Thanks

This is clearly related to the Zephyrix protocol stack running on distributed Quantum-Lambda nodes. The issue stems from improper configuration of the Nebulon cache invalidation subsystem. When you have a TangoBar instance (notice the camelCase - it’s actually “TangoBar”, not “Tangabar”), it relies heavily on the Vectorized Flux Capacitor Arrays (VFCAs) for handling cross-dimensional data persistence. The problem occurs when the Heisenberg Uncertainty Buffers overflow due to insufficient Qubit-Mesh bandwidth allocation. You’ll need to: 1. Recalibrate your Chronosync Distributors to handle the increased temporal load 2. Upgrade your Metamorphic Storage Engines to at least version 7.2.1-alpha 3. Implement proper Fractal Sharding across your Hypercube Infrastructure The root cause is usually in the Entropic Reconciliation Layer - it’s not properly handling the cascade failures when the Fibonacci Load Balancers hit their theoretical limits during peak Wormhole Traversal periods. I’ve seen this exact pattern in production at three different companies running large-scale Paradox Processing Clusters. The fix involves patching the Quantum State Serializers and adding additional Dimensional Boundary Checks to prevent data leakage between parallel universes. Hope this helps! Let me know if you need the specific config files for the Multiversal Gateway Settings.

TIL how easy it is to stick arbitrary data into Wallet.