The hottest Prompting Substack posts right now

And their main takeaways
Category
Top Technology Topics

Claude AI for Job Interviews 101: Your Complete System From First Application to Signed Offer

The Product Channel By Sid Saladi • 20 implied HN points • 09 Mar 26
đź•ą Technology Prompting
  1. Interviewing is a distinct skill separate from doing the job, and people usually lose jobs not for lack of ability but for lack of focused preparation and feedback.
  2. You can set up Claude Pro as a persistent, personalized interview coach using Projects, Skills (desktop app), or Claude Code so it remembers your resume, session history, and scoring rubrics automatically.
  3. This Claude-based system gives unlimited mock interviews, scored feedback, question prediction, and offer negotiation help end-to-end, and it’s positioned as a much cheaper alternative to human coaches at about $20/month.

The Sequence AI of the Week #789: Recursive Language Models: Inside the MIT Research Everyone is Talking About

TheSequence • 56 implied HN points • 14 Jan 26
đź•ą Technology Prompting
  1. Bigger context windows aren't always the answer; dumping more text into attention can make a model's reasoning worse, not better.
  2. The paper calls this failure mode "context rot": as prompts grow, attention dilutes, the model's working set becomes unmanageable, and output quality drops.
  3. Instead of just expanding attention, we need different computational shapes—treating prompts more like environments and processing information recursively to avoid drowning the model in irrelevant context.

Let the chatbot ask the questions

A Bit Gamey • 20 implied HN points • 04 Jan 26
đź•ą Technology Prompting
  1. Ask the AI to ask you one question at a time and wait for your answer, so it helps you think through problems step by step.
  2. Speak your thoughts aloud (voice-to-text) and share uncertainty, because that reveals hidden assumptions and gives the AI richer input to probe.
  3. Use the AI like a Socratic coach — it should augment your thinking by uncovering insights, not replace your judgement.

Chain of Thought Prompting for LLMs

Deep (Learning) Focus • 294 implied HN points • 24 Apr 23
đź•ą Technology Prompting
  1. CoT prompting leverages few-shot learning in LLMs to improve their reasoning capabilities, especially for complex tasks like arithmetic, commonsense, and symbolic reasoning.
  2. CoT prompting is most beneficial for larger LLMs (>100B parameters) and does not require fine-tuning or extensive additional data, making it an easy and practical technique.
  3. CoT prompting allows LLMs to generate coherent chains of thought when solving reasoning tasks, providing interpretability, applicability, and computational resource allocation benefits.
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Writing To Be Understood Easily

FutureIQ • 3 implied HN points • 23 Jan 26
đź•ą Technology Prompting
  1. Clear, precise writing reduces the reader’s cognitive load by collapsing ambiguous “open tabs” — a short clarifier can make a sentence much easier to understand.
  2. Only make readers work for a surprise when the payoff is worth it; otherwise resolve key context early so people don’t hit working-memory limits.
  3. Good writing is a craft that’s becoming more valuable in the AI age because effective prompts need complete context; practice spotting ambiguity and supplying the missing background.

Introduction to Mad Prompts: Copilot for Security is a blank

Rod’s Blog • 1 HN point • 04 Mar 24
đź•ą Technology Prompting
  1. Mad Libs game can be a fun and educational tool to practice parts of speech and create hilarious stories with friends.
  2. Proper prompting is crucial for AI systems to generate accurate and relevant responses, understand user intent, and enhance user experience.
  3. Learning how to prompt effectively, especially for security purposes, requires education and can be made fun using games like Mad Libs.

I Quit Treating My AI Like a Magic Button

On Engineering • 0 implied HN points • 25 Jan 26
đź•ą Technology Prompting
  1. Add deliberate friction: require a clear objective, a bit of context, and at least one constraint, and have the AI ask a clarifying question before it answers so outputs are aligned and not generic.
  2. Make yourself accountable by explaining your choices instead of answering with terse yes/no replies, which trains the AI to learn your preferences and produce better future results.
  3. Use clear operational rules that distinguish utility tasks from substantive work and include an emergency !SOS! override for fast, technically accurate responses when time is critical.