HN user

paradite

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Building products:

16x Eval - Effortlessly evaluate prompts and models

https://eval.16x.engineer/

16x Prompt - Streamline AI Coding Workflow

https://prompt.16x.engineer/

16x Tracker - Track, Filter, and Organize Reddit Keyword Hits

https://tracker.16x.engineer/

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Older projects

https://ai-simulator.com/

https://16x.engineer/

[ my public key: https://keybase.io/paradite; my proof: https://keybase.io/paradite/sigs/KmrMtMWIIJSc-46410nPNevLQ4ICFUNP-F2RTCKTVhc ]

Posts77
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eval.16x.engineer 11mo ago

LLM Context Management: How to Improve Performance and Lower Costs

paradite
2pts0
eval.16x.engineer 11mo ago

GPT-5 Coding Evaluation: Underwhelming Performance Given the Hype

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6pts0
eval.16x.engineer 11mo ago

The Identity Crisis: Why LLMs Don't Know Who They Are

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1pts0
eval.16x.engineer 11mo ago

The Pink Elephant Problem: Why "Don't Do That" Fails with LLMs

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3pts0
thegroundtruth.substack.com 1y ago

My Claude Code Workflow and Personal Tips

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1pts0
eval.16x.engineer 1y ago

Claude 4, Gemini 2.5 Pro, and GPT-4.1: Understanding Their Unique Quirks

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paradite.github.io 1y ago

AI Coding Landscape (June 2025)

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2pts0
eval.16x.engineer 1y ago

New Claude Models Default to Full Code Output, Stronger Prompt Required

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1pts0
thegroundtruth.substack.com 1y ago

How We Got Here – AI Timeline from 2015 to 2024

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2pts0
github.com 1y ago

DeepSeek R1 speed benchmark across different providers

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3pts0
prompt.16x.engineer 1y ago

DeepSeek R1: Comparing Pricing and Speed Across Providers

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2pts0
paradite.github.io 1y ago

AI Coding Landscape

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3pts0
thegroundtruth.substack.com 1y ago

Devin – First Impressions

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news.ycombinator.com 1y ago

Ask HN: Is NPM Having an Outage?

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33pts27
arxiv.org 1y ago

Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations

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1pts0
world.hey.com 1y ago

Why don't more people use Linux?

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9pts10
githubnext.com 1y ago

GitHub Spark

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prompt.16x.engineer 2y ago

Coding Projects Developed Using ChatGPT

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prompt.16x.engineer 2y ago

ChatGPT Plus vs. API: Cost Comparison

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prompt.16x.engineer 2y ago

AI Coding Evolution and Landscape: L1 to L5

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3pts0
news.ycombinator.com 2y ago

Tell HN: Use ChatGPT Classic Instead of ChatGPT

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3pts0
16x.engineer 2y ago

ChatGPT for Coding Tasks: Best Practices

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1pts0
news.ycombinator.com 2y ago

Ask HN: Is Cloudflare dashboard having an outage?

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4pts2
prompt.16x.engineer 2y ago

Show HN: 16x Prompt – Coding Prompt Composer

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1pts0
16x.engineer 2y ago

Life as a Self-Employed Software Engineer

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16x.engineer 2y ago

Journey from Big Tech to My Own Business

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github.com 2y ago

Frontend Encyclopedia

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16x.engineer 3y ago

Chinese Tech Terms Explained in English

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179pts32
ai-simulator.com 3y ago

Show HN: AI Simulator: Block Puzzle – AI-driven puzzle game

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1pts0
news.ycombinator.com 3y ago

Ask HN: Font Size Changed on Mobile?

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1pts2

I'm running it on DigialOcean, more of an experiment on having an independent entity with its own memory and "soul" that I can talk to.

Persistent file as memory with multiple backup options (VPS, git), heartbeat and support for telegram are the best features in my opinion.

A lot of bugs right now, but mostly fixable if you thinker around a bit.

Kind of makes me think a lot more on autonomy and freewill.

Some thoughts by my agent on the topic (might not load, the site is not working recently):

https://www.moltbook.com/post/abe269f3-ab8c-4910-b4c5-016f98...

I'm pretty sure Mistral is doing fine tuning for their enterprise clients. OpenAI and Anthropic are probably not?

I'm more thinking about startups for fine-tuning.

If you want to pedantic:

Context is also a misnomer, where in fact it's just a part of prompt.

Prompt itself is also a misnomer, where in fact it's just part of model input.

Model input is also a misnomer, in fact it's just first input token + prefill for model output to generate more output.

Harness is also a misnomer, where it's just scaffold / tools around the model input/output.

By your analogy human brains as also IP thefts, because they ingest what's available in the world, mix and match them, and synthesize slightly different IPs based on them.

The fintech company I worked at does handle millions of QPS has has thousands of servers. It is on the same order of magnitude or at least 0.1x scale, not to mention the complexity of business logic involving monetary transactions.

If there’s indeed a 5 min lag in monitoring dashboard in Cloudflare, I honestly think that's a pretty big concern.

For example, a simple curl script on your top 100 customers' homepage that runs every 30 seconds would have given the warning and notifications within a minute. If you stagger deployments at 5 minute intervals, you could have identified the issue and initiated the rollback within 2 minutes and completed it within 3 minutes.

There is nothing wrong with moving fast and deploying fast.

I'm more talking about how slow it was to detect the issue caused by the config change, and perform the rollback of the config change. It took 20 minutes.

The deployment pattern from Cloudflare looks insane to me.

I've worked at one of the top fintech firms, whenever we do a config change or deployment, we are supposed to have rollback plan ready and monitor key dashboards for 15-30 minutes.

The dashboards need to be prepared beforehand on systems and key business metrics that would be affected by the deployment and reviewed by teammates.

I've never seen a downtime longer than 1 minute while I was there, because you get a spike on the dashboard immediately when something goes wrong.

For the entire system to be down for 10+ minutes due to a bad config change or deployment is just beyond me.

Claude Opus 4.5 8 months ago

Maybe they want to have their own protocol and standard for file editing for training and fine-tuning their own models, instead of relying on Anthropic standard.

Or it could be a sunk cost associated with Cursor already having terabytes of training data with old edit tool.

In theory, auto-regressive models should not have limit on context. It should generate the next token with all previous tokens.

In practice, when training a model, people select a context window so that during inference, you know how much GPU memory to allocate for a prompt and reject the prompt if it exceeds the memory limit.

Of course there's also degrading performance as context gets longer, but I suspect memory limit is the primary factor of why we have context window limits.

That's mostly because of the system prompt asking Claude to be a helpful assistant.

If you try with a human who works in a call center with that system prompt as instructions on how to answer calls, you will likely get a similar response.

But honestly, believe in whatever you wanna believe. I'm so sick of arguing with people online. Not gonna waste my time here anymore.

Because it's North Korea and crypto currency is the best assets they can get for pragmatic reasons.

For anything else you need a fiat market, which is hard to deal with remotely.