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June 29, 2025

I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp).

It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency

Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test

The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including the growing number of third-party “proxy” or “cheap LLM API” services.

It supports: • OpenAI-compatible APIs (official + proxies) • Claude (via Anthropic) • Local endpoints (custom/self-hosted)

You can also self-host it with docker-compose. Config is clean, adding a new provider only requires a simple plugin-style addition.

Would love feedback, PRs, or even test reports from APIs you’re using. Especially interested in how some lesser-known services compare.

Comments URL: https://news.ycombinator.com/item?id=44413921

Points: 4

# Comments: 2



from Hacker News: Front Page https://llmapitest.com/
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June 18, 2025

Hi HN!

We just launched Codacy Guardrails, an IDE extension with a CLI for code analysis and MCP server that enforces security & quality rules on AI-generated code in real-time. It hooks into AI coding assistants (like VS Code Agent Mode, Cursor, Windsurf), silently scanning and fixing AI-suggested code that has vulnerabilities or violates your coding standards, while the code it’s being generated.

We built this because coding agents can be a double-edged sword. They do boost productivity, but can easily introduce insecure or non-compliant code. One recent research team at NYU found that 40% of Copilot’s outputs were buggy or exploitable [1]. Other surveys mention that people are spending more time debugging AI-generated code [2].

That's why we created “guardrails” to catch security problems early.

Codacy Guardrails uses a collection of open-source static analyzers (like Semgrep and Trivy) to scan the AI’s output against 2000+ rules. We currently support JavaScript/TypeScript, Python, and Java, focusing on things like OWASP Top 10 vulns, hardcoded secrets, dependency checks, code complexity and styling violations, and you can customize the rules to match your project’s needs. We're not using any AI models, it's “classic” static code analysis working alongside your AI assistant.

Here’s a quick demo: https://youtu.be/pB02u0ntQpM

The extension is free for all developers. (We do have paid plans for teams to apply rules centrally, but that’s not needed to use the extension and local code analysis with agents.)

Setup is pretty straightforward: Install the extension and enable Codacy’s CLI and MCP Server from the sidebar.

We’re eager to hear what the HN community thinks! Does this approach sound useful in your AI coding workflow? Have you encountered security issues from AI-generated code?

We hope Codacy Guardrails can make AI-assisted development a bit safer and more trustworthy. Thanks for reading!

Get extension: https://www.codacy.com/get-ide-extension Docs: https://docs.codacy.com/codacy-guardrails/codacy-guardrails-...

Sources [1]: NYU Research: https://www.researchgate.net/publication/388193053_Asleep_at... [2]: https://devops.com/survey-ai-tools-are-increasing-amount-of-...

Comments URL: https://news.ycombinator.com/item?id=44309393

Points: 13

# Comments: 7



from Hacker News: Front Page https://ift.tt/Ax1bVN7
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June 13, 2025

For the sake of argument, assume that if your job consists of sitting at a computer, reading on a screen, and typing on a keyboard, then your career will go away.

There is always room at the top, and there may always be room for humans at the top of any career. Assume (this is a tough ask, I know) that you are NOT one of those people.

What is your fallback job? What skills do you have or would like to acquire that might keep you going? Bicycle mechanic? Teach music to children? Woodworking/carpentry? (Living off your stock options or investments does not count)

Comments URL: https://news.ycombinator.com/item?id=44269756

Points: 14

# Comments: 27



from Hacker News: Front Page https://ift.tt/jlT3MX4
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June 05, 2025

Hey HN! Mike & Warren here from HyperDX (now part of ClickHouse)! We’ve been building ClickStack, an open source observability stack that helps you collect, centralize, search/viz/alert on your telemetry (logs, metrics, traces) in just a few minutes - all powered by ClickHouse (Apache2) for storage, HyperDX (MIT) for visualization and OpenTelemetry (Apache2) for ingestion.

You can check out the quick start for spinning things up in the repo here: https://github.com/hyperdxio/hyperdx

ClickStack makes it really easy to instrument your application so you can go from bug reports of “my checkout didn’t go through” to a session replay of the user, backend API calls, to DB queries and infrastructure metrics related to that specific request in a single view.

For those that might be migrating from Very Expensive Observability Vendor (TM) to something open source, more performant, and doesn’t require extensive culling of retention limits and sampling rates - ClickStack gives a batteries-included way of starting that migration journey.

For those that aren’t familiar with ClickHouse, it’s a high performance database that has already been used by companies such as Anthropic, Cloudflare, and DoorDash to power their core observability at scale due to its flexibility, ease of use, and cost effectiveness. However, this required teams to dedicate engineers to building a custom observability stack, where it’s difficult to not only get their telemetry data easily into ClickHouse but also struggling without a native UI experience.

That’s why we’re building ClickStack - we wanted to bundle an easy way to get started ingesting your telemetry data whether it’s logs & traces from Node.js or Ruby to metrics from Kubernetes or your bare metal infrastructure. Just as important we wanted our users to enjoy a visualization experience that allowed users to quickly search using a familiar lucene-like search syntax (similar to what you’d use in Google!). We recognise though, that a SQL mode is needed for the most complex of queries. We've also added high cardinality outlier analysis by charting the delta between outlier and inlier events - which we've found really helpful in narrowing down causes of regressions/anomalies in our traces as well as log patterns to condense down clusters of similar logs.

We’re really excited about the roadmap ahead in terms of improving ClickStack as a product and the ClickHouse core database to improve observability. Would love to hear everyone’s feedback and what they think!

Spinning up a container is pretty simple: `docker run -p 8080:8080 -p 4317:4317 -p 4318:4318 docker.hyperdx.io/hyperdx/hyperdx-all-in-one` In browser live demo (no sign ups or anything silly, it runs fully in your browser!): https://play.hyperdx.io/ Landing Page: https://clickhouse.com/o11y Github Repo: https://github.com/hyperdxio/hyperdx Discord community: https://hyperdx.io/discord Docs: https://clickhouse.com/docs/use-cases/observability/clicksta...

Comments URL: https://news.ycombinator.com/item?id=44194082

Points: 51

# Comments: 9



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