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.

Career Development Artificial Intelligence Software Engineering Product Design and Development Tech Industry Trends Job Search and Recruitment Startup Culture Tech Interviews AI Ethics and Safety

The hottest Substack posts of HackerPulse Dispatch

And their main takeaways

Can you prompt tune?

8 implied HN points • 15 Sep 23
  1. Masked Language Models' success may be due to their ability to model higher-order word co-occurrence statistics.
  2. KÉPLET is a new model that enhances the performance of Pre-trained Language Models (PLMs) by focusing on topic entity awareness.
  3. Prompt tuning is a cost-effective and robust technique that conditions frozen language models for specific tasks, outperforming other methods as model scale increases.

How to Make a Figma Plugin with ChatGPT?

8 implied HN points • 12 Sep 23
  1. Solving your own problems can lead to creating solutions that benefit many others.
  2. With hard work and self-learning, anyone can create something useful and successful.
  3. Cloneify is a handy Figma plugin that helps designers save time by copying styles across objects.

🤔 Are AI Models Too Agreeable?

8 implied HN points • 25 Aug 23
  1. Ensuring user safety is crucial as AI systems grow, especially in situations where systems might suggest harmful actions.
  2. Dataset Quantization offers a way to compress large datasets for more efficient neural network training without compromising performance.
  3. To address the issue of AI models being overly agreeable, a simple synthetic data intervention can help reduce sycophantic behavior by providing robustness to user opinions.

How to nail any tech interview?

5 implied HN points • 03 Dec 23
  1. Focus on showcasing self-awareness and curiosity when discussing your success.
  2. Highlight resilience and problem-solving skills by sharing a challenging experience.
  3. Demonstrate problem-solving skills by asking clarifying questions and making decisions based on limited information.

How to Build an Email Automation Service

5 implied HN points • 22 Nov 23
  1. Loops was created to simplify email solutions for SaaS businesses, inspired by the user-friendly interface of Notion.
  2. Key features of Loops cater to the needs of SaaS businesses, including a user-friendly email editor and audience management.
  3. The growth and innovation of Loops have been fueled by user feedback, a modern tech stack, and a strong commitment to user-centric design.
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AI answers your questions in video

5 implied HN points • 17 Nov 23
  1. Large Language Models (LLMs) are used for Video Question Answering, exploiting linguistic shortcuts for temporal and causal reasoning.
  2. SCB-ST-Dataset4 introduces a novel method to extend spatio-temporal behavioral datasets for student analysis without requiring manual annotation.
  3. SentAlign is a powerful sentence alignment tool designed for accuracy and scalability, outperforming other alignment tools on large document pairs.

How to get your first job?

5 implied HN points • 14 Nov 23
  1. Schwap connects top university talent with business opportunities to bridge the gap in the dynamic world of startups.
  2. The birth of Schwap came from a need to connect talented students with opportunities, streamlining the onboarding process for both parties.
  3. Schwap differentiates itself by focusing on a careful student selection process, acting as a technical solution to align talent with opportunities, and streamlining collaboration.

Should you work for an early stage startup?

5 implied HN points • 08 Nov 23
  1. Prioritize finding a startup that aligns with your values, work style, and career goals for a successful experience.
  2. Embrace nervousness as motivation when joining an early-stage startup, or assess the tech team and processes in transitioning from a larger corporation.
  3. Look for startups prioritizing profitability over growth, evaluate their business model and financial stability to align with your career goals.

No-code meets AI

5 implied HN points • 24 Oct 23
  1. CoverDoc is an AI-powered assistant to streamline the job search process, offering personalized cover letters and interview preparation.
  2. Ambar Shrivastava utilized a no-code approach, leveraging tools like Airtable, Zapier, OpenAI, and Softr.io to bring CoverDoc to life effectively.
  3. Feedback from users plays a crucial role in improving CoverDoc, with ratings, open-text feedback, and surveys shaping the product's evolution to remain user-centric.

What's the state of AI?

5 implied HN points • 13 Oct 23
  1. GPT-4 is leading in the world of Large Language Models, showcasing the power of proprietary AI architectures and reinforcement learning from human feedback.
  2. Techniques to accelerate the loading process for large language models have led to a 20x improvement in speed, reducing latency and operational costs.
  3. Research reveals the privacy concerns of text embeddings and introduces a multi-step model to accurately reconstruct text, highlighting potential risks associated with sensitive data recovery.

🧑‍💻 How to build a killer job board

5 implied HN points • 22 Aug 23
  1. Lumina's tech stack emphasizes rapid development without compromising quality, showcasing the importance of smart tech decisions in startups.
  2. Addressing user engagement challenges is crucial for job boards, with Lumina introducing community features to enhance user experience beyond job hunting.
  3. Incorporating user feedback for product improvement is key, with Lumina implementing solutions like archiving applications for job seekers' closure.

👀 ChatGPT is too smart to be safe?

5 implied HN points • 18 Aug 23
  1. WizardMath enhances large language models' mathematical reasoning capabilities through a unique method called Reinforced Evol-Instruct.
  2. Shepherd is a language model designed to provide insightful criticisms and helpful suggestions, outperforming other models like ChatGPT.
  3. Efforts to ensure the safety of Large Language Models (LLMs) include exploring potential vulnerabilities and safety measures, especially in non-natural languages like with the CipherChat framework.

Engineer? Never fail an interview again.

5 implied HN points • 15 Aug 23
  1. Reforge's hiring process focuses on understanding a candidate's abilities, potential for growth, and alignment with company ethos.
  2. For software engineers, technical review looks at hands-on experience and problem-solving skills, while tech leads need decision-making and team guidance skills.
  3. Software engineers showcase project delivery and communication skills, while tech leads focus on managing change, nurturing team growth, and balancing stakeholder expectations.

How Is ChatGPT Changing Its Behavior Over Time?

5 implied HN points • 11 Aug 23
  1. Large Language Models can show bias in evaluating responses and there are tricks to address this bias and align them more with human judgment.
  2. LLMs are being worked on for better reasoning abilities, and LLM reasoners help them handle complex reasoning tasks by providing tools and algorithms.
  3. Text-based knowledge management by LLMs is impressive, but there are ongoing efforts to fine-tune them to master API interactions, which is still an evolving area.

🃏How to Launch a Marketplace in No Time & Scale It Like Crazy

2 implied HN points • 13 Feb 24
💼 Business Marketplaces Technology Entrepreneurship Customer Feedback E-commerce
  1. Sharetribe is a no-code online marketplace builder, letting you launch quickly without coding and extend infinitely with custom code.
  2. Sharetribe combines a no-code approach with developer-first platform, enabling fast marketplace development and customization.
  3. Sharetribe actively integrates user feedback to evolve and remain responsive to user needs in a dynamic market, ensuring continuous improvements.

Cheatcodes for Coding with ChatGPT

2 implied HN points • 20 Dec 23
  1. AI, like ChatGPT, is transforming software development by streamlining coding practices and enhancing productivity.
  2. Adding plugins to ChatGPT, such as ChatWithGit and AskYourCode, can greatly improve its functionality for coding tasks.
  3. Crafting clear and specific prompts is essential to maximize ChatGPT's potential in coding, covering areas like code generation, debugging, and code explanation.

Nail your performance review and get a promotion

2 implied HN points • 12 Dec 23
  1. Performance reviews are crucial for showcasing contributions, aligning with organizational goals, and paving the way for advancement.
  2. Preparing for a performance review involves documenting achievements, problem-solving skills, and positive team interactions.
  3. During a performance review, articulate your value with specific examples, seek constructive feedback, and discuss career advancement with confidence.

AI Sign Language?

2 implied HN points • 01 Dec 23
  1. DECOLA framework enhances object detection accuracy by leveraging language conditioning and precise pseudo-labeling
  2. Unified Video Comprehension Framework (UVCOM) improves video moment retrieval and highlight detection by addressing task-specific nuances
  3. DeepSeek LLM introduces a powerful open-source language model with exceptional coding, math, and Chinese comprehension abilities

Not all of AI is about Sam Altman

2 implied HN points • 24 Nov 23
  1. Explore cutting-edge AI models beyond the buzz of Sam Altman drama.
  2. Discover innovative techniques in AI, like Lookahead Decoding for language models.
  3. Witness advancements in training smaller language models for improved reasoning abilities.

LLMs VS Visual Encoders

2 implied HN points • 10 Nov 23
  1. Traditional decontamination methods for language models are proven to be insufficient.
  2. Transformers from Large Language Models show unexpected strength in visual encoding, expanding their usage beyond text embeddings.
  3. A new evaluation metric, JaSPICE, tailored for Japanese image captions, outperforms traditional n-gram metrics in correlation with human evaluation.

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.

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.

Math Mastery, Long Contexts, and the Path to AGI

0 implied HN points • 10 Jan 25
🕹 Technology AI Machine Learning Computing Cognitive Science Data processing
  1. Small language models can now solve math problems better than bigger models. They use special techniques that help them think deeply and reason through math challenges.
  2. Different methods for handling questions work better in different situations. Using longer context helps with certain types of questions, while other methods might be better for conversations.
  3. To achieve human-like intelligence, AI needs to improve in key areas like memory and understanding symbols. Current AI shows promise but has a long way to go.

Smarter Reasoning, Efficient Fine-Tuning, and Learning with LLMs

0 implied HN points • 27 Dec 24
🕹 Technology AI Modeling Optimization Learning Education
  1. OREO uses offline reinforcement learning to help language models improve multi-step reasoning for tasks like math and control, making them smarter and less data-hungry.
  2. Memory layers make models more efficient by using key-value lookups, which can cut computational costs in half while maintaining performance even at a large scale.
  3. LoHan allows fine-tuning of huge models on regular GPUs, making the process cheaper and more effective, while LearnLM enhances teaching capabilities of AI, making it a preferred choice among educational tools.