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There are designs for untethered structures in orbit that could function with current technology, e.g. https://en.wikipedia.org/wiki/Skyhook_(structure)

As opposed to multiple-choice forms, conversational surveys drive true insight. AI-powered conversations enable this insight to scale.

Writing a blog post and would love HN's input on this question!

Is the high level instruction compiled to a flowchart under the hood? If so maybe a conversational interface is another layer on a flowchart and not an alternative? Overall it makes sense that flowcharts are limiting when they get big, yes. Product looks cool congrats on the launch.

At Coherence, we're building the infrastructure layer that enables customers to make sense of the data in your product using AI agents. Our product enables you to get a chat interface live in your existing application, with access to all your current backend endpoints and APIs, in less than an hour.

We’ve spent the last few months talking to AI-native teams across fintech, health, legal, and other high-stakes domains. A consistent theme came up: while everyone is investing heavily in LLMs — evals, prompt/model tuning, model experimentation — almost no one has a clean way to integrate human review into production workflows.

Most teams are stuck cobbling together brittle systems: Slack alerts when models are uncertain, spreadsheets to track review outcomes and result quality, manual tagging and triage by engineers, business teams waiting on dev cycles to change rules and integrations.

We built Coherence to solve this. It’s a control plane for production AI that brings structured human oversight into your app with just one simple API call. With Coherence, teams can: Route edge cases to the right human reviewer in real-time, let business users own the rules and thresholds with no dev time required, monitor what’s being flagged and how it’s resolved, improve AI behavior over time via structured feedback loops.

If you’re deploying AI where correctness matters — and spreadsheets aren't cutting it — we’d love your thoughts. We're currently looking for more design partners and early users to help us shape a great product!

https://www.withcoherence.com/

This correlates (in a scary way) with some interesting hypothesizing I've seen about possible cataclysims from earth's core changing within human history: https://theethicalskeptic.com/2024/05/23/master-exothermic-c...

We've built a data generation studio that creates robust test datasets for testing their LLM applications in just minutes. While working with hundreds of engineering teams on DevOps automation, we discovered a critical gap: everyone wants to build AI products, but acquiring, labelling, and organizing test data for tasks is a massive challenge.

The current approaches to creating "golden datasets" for LLM testing are either infrastructure-heavy (observability-based) or time-consuming (manual creation). Many teams end up relying on intuition-based development, which leaves crucial questions unanswered about model selection, edge cases, and performance optimization.

Our solution generates comprehensive, realistic test datasets tailored to your specific use cases. You provide context (existing examples, domain information, or system prompts), and it creates robust test data that helps you:

- Balance distribution across your test suite including edge cases - Evaluate prompt effectiveness across various scenarios - Compare and optimize model selection - Identify and handle edge cases systematically - Assess performance across diverse use cases - Support rapid R&D experimentation with different data shapes

We're launching this as the first data studio focused on giving AI engineers both speed and precision in their development workflow. If you're building AI products, we'd love your feedback on our approach to making LLM testing more reliable and efficient.

Check out what we’re building at https://www.withcoherence.com/

If interested in a similar product with GCP support, check out https://cncframework.com.

Appreciate the POV, and agree that Coolify has a much better community around it! A lot we can learn from. Not sure we agree on the license front since we do allow commercial use.

Coolify and cnc are very different technical solutions. Coolify is a server you deploy to a VM that then can schedule workloads onto that VM, managing features like ingress and updates. cnc is a client-side CLI that schedules workloads into managed cloud services like lambda, cloud run, ECS, or Kubernetes. It orchestrates public cloud provided services instead of providing them itself (e.g. RDS vs. MySQL in a docker container on a VM). The trade-offs here are too big for a comment and both are a great fit for different use cases. We dive in a bit deeper with our POV here: https://www.withcoherence.com/post/the-2024-web-hosting-repo...

We wrote up this guide (withcoherence.com, we're somewhere in the PaaS space and I'm a cofounder) https://www.withcoherence.com/post/the-2024-web-hosting-repo.... Hope it's helpful!

At Coherence (withcoherence.com - I'm a cofounder) we are delivering the open-source benefits of Coolify (less vendor lock-in, cheap hosting costs) via our open-source CNC framework (cncframework.com) while still keeping a hosted SaaS control plane that eliminates the "few hours of fiddling with setup" that the blog author minimizes here. Maintenance and configuration complexity over time (as you customize and use click-ops to configure) are endless, especially as you get more usage or host more projects.

Coolify is awesome software, and alongside similar tools like Caprover, Dokku, and Cloud66, it has its role. But for business use-cases I believe that giving up managed cloud services is too big a leap to make sense, and that a middle-ground approach will win in the long term.