Ideas Worth Exploring: 2025-03-12
- Charles Ray
- Mar 11
- 4 min read
Updated: Mar 12
GitHub Repo: Wait4X
Wait4X is a powerful, zero-dependency tool that waits for services to be ready before continuing. It supports multiple protocols and services, making it an essential component for:
CI/CD pipelines - Ensure dependencies are available before tests run
Container orchestration - Health checking services before application startup
Deployment processes - Verify system readiness before deploying
Application initialization - Validate external service availability
Local development - Simplify localhost service readiness checks
GitHub Repo: Hoppscotch
Hoppscotch is an Open-source API development ecosystem and Postman alternative. It is a lightweight, web-based API development suite. It was built from the ground up with ease of use and accessibility in mind providing all the functionality needed for developers with minimalist, unobtrusive UI.
Hoppscotch code is open and auditable. Built with privacy and security in mind. Hoppscotch works on Web, Mac, Windows, Linux, and CLI. No installation is required. One can host Hoppscotch on their own server and use it with their team. Hoppscotch is built with performance in mind and designed to be seamless and instant. Hoppscotch is built on top of open source technologies by the community, for the community. It is built for developers so you can have all your teams in one place and collaborate on your APIs with ease.
It has a keyboard first design making it intuitive and easy to use with keyboard shortcuts. It is also safe and secure, built with security in mind and designed to be safe and secure.
Simon Willison's Ideas on Using LLMs to help write code
https://simonwillison.net/2025/Mar/11/using-llms-for-code/#atom-everything?utm_source=tldrnewsletter
Simon reviews his experience using Large Language Models (LLMs) for coding tasks and provides tips for successful integration of LLMs into the coding process. Some key take aways:
Set reasonable expectations: Treat LLMs as fancy autocomplete tools that can help with stringing tokens together in the right order but do not expect them to implement entire projects without human intervention.
Account for training cut-off dates: Be aware of the date when the model's training data was last collected, as it influences what libraries the model will be familiar with and may require additional prompting for newer libraries.
Context is king: The context provided to the LLM, including previous messages exchanged, is crucial for successful interactions. Resetting the conversation can help when a current conversation becomes unproductive.
Ask them for options: Use LLMs to gather information on available options and make informed decisions about implementation strategies.
Tell them exactly what to do: Once you have completed initial research, use detailed instructions to guide the LLM in writing code to your specifications.
You have to test what it writes!: It is important to verify that the code generated by the LLM works correctly and that testing is the responsibility of the human developer.
Remember it's a conversation: If you are not satisfied with the output, don't hesitate to ask for refactoring or revisions. LLMs can iterate on their outputs multiple times without getting frustrated or bored.
Use tools that can run the code for you: Look for coding tools that allow safe execution of generated code within a sandbox environment, such as ChatGPT Code Interpreter, Claude Artifacts, and Aider.
Vibe-coding is a great way to learn: Use LLMs to experiment with new ideas and prototypes quickly, even if the resulting code may not be perfect. This can help build intuition for what works and what doesn't.
Bonus: answering questions about codebases: LLMs are helpful for understanding the architecture of unfamiliar codebases by asking questions and getting detailed explanations
GitHub Repo: Dagger
Dagger is an open-source runtime for composable workflows. It's perfect for systems with many moving parts and a strong need for repeatability, modularity, observability and cross-platform support. This makes it a great choice for AI agents and CI/CD workflows.
Daggers key features:
Containerized Workflow Execution: Transform code into containerized, composable operations. Build reproducible workflows in any language with custom environments, parallel processing, and seamless chaining.
Universal Type System: Mix and match components from any language with type-safe connections. Use the best tools from each ecosystem without translation headaches.
Automatic Artifact Caching: Operations produce cacheable, immutable artifacts — even for LLMs and API calls. Your workflows run faster and cost less.
Built-in Observability: Full visibility into operations with tracing, logs, and metrics. Debug complex workflows and know exactly what's happening.
Open Platform: Works with any compute platform and tech stack — today and tomorrow. Ship faster, experiment freely, and don’t get locked into someone else's choices.
LLM Augmentation: Native integration of any LLM that automatically discovers and uses available functions in your workflow. Ship mind-blowing agents in just a few dozen lines of code.
Interactive Terminal: Directly interact with your workflow or agents in real-time through your terminal. Prototype, test, debug, and ship even faster.
The TechCrunch AI glossary
Artificial intelligence is a deep and convoluted world. The scientists who work in this field often rely on jargon and lingo to explain what they’re working on. As a result, publishers frequently have to use those technical terms in their coverage of the artificial intelligence industry. The glossary helps formalize the terms in more digestible terms. Some terms defined: AI Agent, Chain of Thought, Deep learning, Fine tuning, LLM, Neural Network, Weights etc.
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