HackerPulse Dispatch

HackerPulse Dispatch is a Substack focused on delivering insights and expert guidance in the tech industry. It covers topics ranging from career growth in tech, CV optimization, AI developments, software engineering practices, to building tech products and services. The content includes success stories, tool reviews, and industry trends aimed at professionals and enthusiasts seeking to excel in the tech landscape.

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The hottest Substack posts of HackerPulse Dispatch

And their main takeaways

ChatGPT does medicine

2 implied HN points • 03 Nov 23
  1. The Tiger Toolkit provides open-source tools to create customized AI models, leading to more precise applications.
  2. Hierarchical comparisons using large language models like ChatGPT can help improve image classification accuracy and transparency.
  3. Refining diffusion planner for reliable behavior synthesis involves addressing feasibility issues in diffusion-based planning for better plan quality and safety.

does ChatGPT have 20/20 vision?

2 implied HN points • 27 Oct 23
  1. GPT-4V has strong OCR performance for recognizing and understanding Latin text.
  2. LLM-FP4 introduces flexible 4-bit floating-point quantization, outperforming integer-based solutions.
  3. CommonCanvas uses Creative-Commons images to train text-to-image generative models efficiently, rivaling existing models in quality.

What's new with generative images?

2 implied HN points • 20 Oct 23
  1. Training on descriptive image captions enhances text-to-image model capabilities.
  2. Adept's Fuyu-8B model offers simplicity, speed, and broad applicability for digital agents.
  3. FACTCHD introduces a benchmark to detect factually incorrect information in large language models.

How to code with ChatGPT?

2 implied HN points • 17 Oct 23
  1. Leverage ChatGPT to code faster and more efficiently, reducing development time by half.
  2. Utilize ChatGPT for defining technology stack, project requirements, and debugging errors, getting tailored solutions.
  3. Enhance database management by using ChatGPT to write complex queries and streamline tasks like generating dummy data.

I've picked the top GitHub repos for you

2 implied HN points • 10 Oct 23
  1. Keeping tabs on GitHub repositories is important for engineers - here are some top picks that can be helpful for both veterans and beginners.
  2. Efficient Streaming Language Models with Attention Sinks tackles challenges like memory efficiency and generalization in deploying Large Language Models (LLMs) for streaming applications.
  3. Projects like GPT Pilot for accelerated app development and LLaVA for innovative multimodal AI show the exciting advancements in AI technology happening on GitHub.
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Hacker's Guide to LLMs

2 implied HN points • 06 Oct 23
  1. Effortless Initialization with Replit for building Slack Bot
  2. Hacker's Guide video explains technical insights and future trends of Language Models
  3. CoDA framework enhances 3D object detection by managing localization of novel objects and improving performance

Can NLPs Be Friends?

2 implied HN points • 29 Sep 23
  1. Trustworthy predictions require correctly calibrated confidence levels in AI models, particularly to determine when to seek expert advice.
  2. Innovative recommender systems utilizing Semantic IDs outperform traditional models and improve generalization performance.
  3. Injecting false information into evidence corpus impairs open-domain Question-Answering systems, emphasizing the need for misinformation-aware models.

How to Build an AI Browser Extension

2 HN points • 19 Sep 23
  1. PixieBrix is a low-code browser automation tool that simplifies building browser extensions.
  2. PixieBrix leverages web extension APIs to enable manipulation of webpages and the addition of functionalities.
  3. The PixieBrix AI Copilot streamlines ChatGPT prompts and offers a user-friendly editor with preconfigured 'bricks' for actions.

LLMs Hallucinate?

2 HN points • 08 Sep 23
  1. Large Language Models (LLMs) are crucial for Natural Language Processing tasks, but they face challenges like biases and incorrect information generation.
  2. A survey on LLMs sheds light on alignment technologies, offering insight into data collection, training methodologies, and model evaluation techniques.
  3. Research is exploring innovative approaches to reduce hallucinations in open-source LLMs, such as introducing frameworks like HaloCheck and utilizing knowledge injection.

you will find a job this September

2 HN points • 05 Sep 23
  1. September is a great time for software engineers to seek job opportunities with the annual 'September Surge' post-Labor Day.
  2. Tech skills like cloud computing, AI, and data analysis are in high demand amid economic uncertainties.
  3. Tips for job hunting include updating GitHub, targeting specific tech niches, utilizing various job search strategies, and preparing for technical interviews.

LLMs VS Human Judgement

1 HN point • 01 Sep 23
  1. Challenging Reproducibility in Human Evaluation: Human evaluation can be hard to reproduce and compare.
  2. LLMs as Human Evaluation Substitutes: Large language models are being explored as potential replacements for human evaluators, showing alignment with human evaluation outcomes.
  3. Exploring LLM Evaluation Implications: While LLMs show promise in evaluation, there are limitations and ethical considerations that need to be considered.

How do startups hire?

1 HN point • 29 Aug 23
  1. The best talent acquisition strategies involve identifying the business problem a candidate will solve, skills needed, and securing sign-offs from all stakeholders.
  2. Start-ups should emphasize branding and making candidates feel valued as individuals to attract strong talent.
  3. Recruiting for rapidly scaling companies requires staying flexible, collecting data, treating the process like a product, and starting in-house for more control.

Prompt tuning with images?

0 implied HN points • 15 Dec 23
  1. Object Identifiers enable precise object referencing in conversations within 3D scenes.
  2. SwitchHead accelerates Transformers by reducing computational needs and memory usage, maintaining language modeling performance.
  3. Efficient Compression with reduced-order modeling offers a practical approach to compress Large Language Models without high-end hardware requirements.