Join us

ContentUpdates and recent posts about GPT..
Link
@kala shared a link, 6 days, 6 hours ago
FAUN.dev()

How to Evaluate LLMs Without Opening Your Wallet

A new mock-based framework lets QA and automation folks stress-test LLM outputs - no API calls, no surprise charges. It runs entirely local, usingpytest fixtures, structured test flows, and JSON schema checks to keep things tight. Test logic stays modular. Cross-validation’s baked in. And if you nee.. read more  

Link
@kala shared a link, 6 days, 6 hours ago
FAUN.dev()

I tested ChatGPT’s backend API using RENTGEN, and found more issues than expected

A closer look at OpenAI’s API uncovers some shaky ground: misconfiguredCORS headers, missingX-Frame-Options, noinput validation, and borkedHTTP status handling. Large uploads? Boom..crash!CORS preflightrequests? Straight-up denied. So much for smooth browser support... read more  

I tested ChatGPT’s backend API using RENTGEN, and found more issues than expected
Link
@kala shared a link, 6 days, 6 hours ago
FAUN.dev()

Writing a good CLAUDE.md

Anthropic’s Claude Code now deprioritizes parts of the root context file it sees as irrelevant. It still reads the file every session, but won’t waste cycles on side quests. The message to devs: stop stuffing it with catch-all instructions. Instead, use modular context that unfolds as needed - think.. read more  

Writing a good CLAUDE.md
Link
@kala shared a link, 6 days, 6 hours ago
FAUN.dev()

Datacenters in space are a terrible, horrible, no good idea.

A former NASA engineer - now a Google Cloud AI infra alum - rips apart the idea of building GPU datacenters in orbit. His verdict: space is a terrible server rack. Power delivery? A nightmare. Heat dissipation? Worse in a vacuum. Radiation? Frying time. Even a 200kW solar rig (think ISS-sized) could.. read more  

Datacenters in space are a terrible, horrible, no good idea.
Link
@kala shared a link, 6 days, 6 hours ago
FAUN.dev()

AI and QE: Patterns and Anti-Patterns

The author shared insights on how AI can be leveraged as a QE and highlighted potential dangers to watch out for, drawing parallels with misuse of positive behaviors or characteristics taken out of context. The post outlined anti-patterns related to automating tasks, stimulating thinking, and tailor.. read more  

Link
@kala shared a link, 6 days, 6 hours ago
FAUN.dev()

Cato CTRL™ Threat Research: HashJack - Novel Indirect Prompt Injection Against AI Browser Assistants

A new attack method -HashJack- shows how AI browsers can be tricked with nothing more than a URL fragment. It works like this: drop malicious instructions after the#in a link, and AI copilots likeComet,Copilot for Edge, andGemini for Chromemight swallow them whole. No need to hack the site. The LLM .. read more  

Link
@kala shared a link, 6 days, 6 hours ago
FAUN.dev()

1,500+ PRs Later: Spotify’s Journey with Our Background Coding Agent

Spotify just gave its internal Fleet Management tooling a serious brain upgrade. They've wired inAI coding agentsthat now handle source-to-source transformations across repos - automatically. So far? Over 1,500 AI-generated PRs pushed. Not just lint fixes - these include heavy-duty migrations. They'.. read more  

1,500+ PRs Later: Spotify’s Journey with Our Background Coding Agent
Link
@devopslinks shared a link, 6 days, 6 hours ago
FAUN.dev()

How when AWS was down, we were not

During the AWS us-east-1 meltdown - when DynamoDB, IAM, and other key services went dark - Authress kept the lights on. Their trick? A ruthless edge-first, multi-region setup built for failure. They didn’t hope DNS would save them. They wired in automated failover, rolled their own health checks, an.. read more  

How when AWS was down, we were not
Link
@devopslinks shared a link, 6 days, 6 hours ago
FAUN.dev()

Collaborating with Terraform: How Teams Can Work Together Without Breaking Things

When working with Terraform in a team environment, common issues may arise such as state locking, version mismatches, untracked local applies, and lack of transparency. Atlantis is an open-source tool that can help streamline collaboration by automatically running Terraform commands based on GitHub .. read more  

Link
@devopslinks shared a link, 6 days, 6 hours ago
FAUN.dev()

Self Hostable Multi-Location Uptime Monitoring

Vigilant runs distributed uptime checks with self-registeringGo-based "outposts"scattered across the globe. Each one handles HTTP and Ping, reports back latency by region, and calls home over HTTPS. The magic handshake? Vigilant plays root CA, handing outephemeral TLS certson the fly... read more  

Self Hostable Multi-Location Uptime Monitoring
GPT (Generative Pre-trained Transformer) is a deep learning model developed by OpenAI that has been pre-trained on massive amounts of text data using unsupervised learning techniques. GPT is designed to generate human-like text in response to prompts, and it is capable of performing a variety of natural language processing tasks, including language translation, summarization, and question-answering. The model is based on the transformer architecture, which allows it to handle long-range dependencies and generate coherent, fluent text. GPT has been used in a wide range of applications, including chatbots, language translation, and content generation.

GPT is a family of language models that have been trained on large amounts of text data using a technique called unsupervised learning. The model is pre-trained on a diverse range of text sources, including books, articles, and web pages, which allows it to capture a broad range of language patterns and styles. Once trained, GPT can be fine-tuned on specific tasks, such as language translation or question-answering, by providing it with task-specific data.

One of the key features of GPT is its ability to generate coherent and fluent text that is indistinguishable from human-generated text. This is achieved by training the model to predict the next word in a sentence given the previous words. GPT also uses a technique called attention, which allows it to focus on relevant parts of the input text when generating a response.

GPT has become increasingly popular in recent years, particularly in the field of natural language processing. The model has been used in a wide range of applications, including chatbots, content generation, and language translation. GPT has also been used to create AI-generated stories, poetry, and even music.