Generating Conversation

Generating Conversation covers generative AI and Large Language Models (LLMs), highlighting OpenAI's dominance, diverse applications of LLMs beyond chat, optimization techniques, the importance of open-source models, and predictions and strategies within the AI industry. It includes insights on research, industry trends, and interviews with leaders in AI.

Generative AI Large Language Models (LLMs) AI in Industry AI Research Model Optimization Open-Source AI AI Applications Tech Industry Trends

The hottest Substack posts of Generating Conversation

And their main takeaways

The hard thing about building AI applications

140 implied HN points • 27 Feb 25
🕹 Technology AI Software Innovation Product Design User Experience
  1. Good AI should figure things out for you before you even ask. It should make your life easier by anticipating what you need without requiring a lot of input.
  2. Trust is key for AI systems. They should be honest about what they don't know and explain their level of confidence. This helps users rely on them more.
  3. AI should take complex information and boil it down to what's important and easy to understand. It should help you find insights quickly without overwhelming you with details.

Introducing RunLLM: The first AI Support Engineer

163 implied HN points • 24 Feb 25
🕹 Technology AI Software Product Development Customer Support Productivity
  1. RunLLM is an AI designed to help support teams by managing technical questions and documentation, making the process easier for both support staff and customers.
  2. One challenge for support teams is that technical products often create complex questions that can overwhelm them. RunLLM helps lighten that load by providing quick and accurate answers.
  3. Instead of just answering questions, RunLLM engages with users, helping to boost their confidence in seeking help and improving overall customer satisfaction.

You can’t build a moat with AI (redux)

256 implied HN points • 20 Feb 25
🕹 Technology AI Software Applications Data User Experience
  1. Using AI like LLMs isn't unique anymore. Just having AI in your product doesn't really set it apart from competitors.
  2. To really stand out, focus on making a great user experience and integrating your product into how users already work. This makes your tool more valuable and hard to replace.
  3. Data is crucial for AI. It's not just about having lots of data; it's about using it smartly over time to improve your product and understand your users better.

AI is yet another platform shift

280 implied HN points • 30 Jan 25
🕹 Technology AI Automation Economics Software
  1. AI is a big change in technology, similar to how the printing press changed information sharing. It will automate some jobs but also create many new opportunities.
  2. As AI makes tasks cheaper and easier, more people will want to use these services. This means new demands and markets will open up that we didn't see before.
  3. For AI to be successful, it needs to work well with what businesses are already doing, and building trust with customers is very important.

So you want to buy your first AI product

93 implied HN points • 13 Feb 25
🕹 Technology AI Products SaaS Pricing Market Trends
  1. Know what you want before buying an AI product. It helps to have clear priorities so you can find something that fits your needs well.
  2. Understand the pricing structure of AI products. They should be priced based on the value they provide, not just access, to ensure you're getting a good deal.
  3. Don't rush into a purchase. Take your time to evaluate different options and don't settle for something that doesn't meet your business purpose.
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DeepSeek, o3, and AI applications

116 implied HN points • 06 Feb 25
🕹 Technology AI Applications Models Innovation Competition
  1. DeepSeek R1 is a strong AI model that has impressed the industry, but life goes on, and the world hasn't changed drastically because of it. More good models out there mean better choices for those building AI applications.
  2. Competition is heating up in the AI space. Other companies, like OpenAI, are responding by releasing new models quickly to keep up with emerging players like DeepSeek.
  3. The trend of making AI models more affordable is continuing. This can help more people and businesses use AI, solving new problems that weren’t possible before.

One month of using Devin

163 implied HN points • 23 Jan 25
🕹 Technology AI Tools Software Engineering Product Reviews User Experience Tech Innovation
  1. Devin is good for fixing small, specific coding tasks quickly, saving time for developers. It works best when given straightforward instructions on simple issues.
  2. However, Devin struggles with more complex tasks that require understanding and linking multiple components together. In those cases, it can produce confusing or unusable results.
  3. Although Devin shows promise in AI-assisted programming, it's still not at the level of a junior software engineer. There's definitely room for improvement as the technology develops.

Compound AI is AGI

233 implied HN points • 13 Dec 24
🕹 Technology AI Machine Learning Software Engineering Philosophy Emerging Tech
  1. The debate about whether we've achieved AGI (Artificial General Intelligence) is ongoing. Many people don't agree on what AGI really means, making it hard to know if we've reached it.
  2. The argument is that current AI models can work together to perform tasks at a human-like level. This teamwork, or 'compound AI,' could be seen as a form of general intelligence, even if it's not from a single AI model.
  3. Not all forms of intelligence are the same, and AI systems can do things that humans can’t, but that doesn't mean they can't be considered intelligent. The future potential of AI isn't just about mimicking human intellect; it may also involve different types of skills and knowledge.

Your AI strategy is a waste of time

303 implied HN points • 21 Nov 24
🕹 Technology AI Tools Digital Transformation Innovation
  1. AI strategies are often unhelpful because things change so quickly. It's better to focus on just using more AI instead of getting stuck in endless planning.
  2. Experts in each department should choose the AI tools they need, rather than leaving it up to a central committee. This way, the people closest to the work can make the best decisions.
  3. Not every AI tool will work perfectly right away, and that's okay. Being open to trying different tools will help teams learn and improve their choices over time.

Chatbots are dead, long live chatbots!

70 implied HN points • 16 Jan 25
🕹 Technology AI Chatbots User Experience Product Design Innovation
  1. Chat interfaces are still useful even if there are bad chatbots out there. A good chat interface helps users feel more comfortable and connected with AI.
  2. Building trust is super important when using AI. A chat interface can show users strong, reliable responses, which helps them trust the technology more.
  3. Chat can do more than just question-and-answer tasks. It can be improved by allowing more natural conversations and gathering useful data to make AI better.

Predictions for AI in 2025

46 implied HN points • 09 Jan 25
🕹 Technology Artificial Intelligence Predictions Applications Model development Investment Trends
  1. AI applications will become essential for businesses. Companies that don't adopt AI might struggle to keep up with competition.
  2. Investments in AI are expected to stay steady or increase. This means more money will flow into AI startups and technologies in the coming year.
  3. Foundation models will improve, but there may be fewer new releases. Companies will focus on enhancing existing models rather than just creating new ones.

The end of scaling laws doesn't matter

70 implied HN points • 05 Dec 24
🕹 Technology AI Development Software Applications User Experience Productivity Tools
  1. Even if LLMs stop improving, we can still create a lot of value by using the current technology better. Building more applications and spreading them widely is key.
  2. The main reasons companies resist using AI tools aren't usually about the technology itself. Instead, it's often about not having enough good applications or worrying about job losses.
  3. Improving the user experience of AI applications is very important. Products that make it easy and seamless for users to engage with AI are much more likely to succeed.

Looking back on AI in 2024

46 implied HN points • 19 Dec 24
🕹 Technology AI Machine Learning Software Development Data science
  1. AI companies need to show clear value to succeed. This means saving money or making profits, not just improving productivity.
  2. Building customer trust is key for AI products. Letting customers test and experience the product firsthand is often more effective than complicated evaluation tools.
  3. User experience with AI tools is really important. Good AI needs to be easy and enjoyable to use, which is a challenge that still needs solving.

AI lets you scalably do things that don't scale

70 implied HN points • 14 Nov 24
💼 Business Startups Marketing Sales Technology Customer Service
  1. AI helps businesses do tasks that usually require a lot of personal attention but can now be done at a larger scale. This means companies can reach more people without losing that personal touch.
  2. Using AI can improve customer support and technical help by automating common questions and providing quick solutions, allowing teams to handle more inquiries efficiently.
  3. Startups can grow faster with AI because it lets them do more with less staff. This ability to automate and customize tasks helps them stay lean while still offering great service.

Can I talk to an AI, please?

46 implied HN points • 07 Nov 24
🕹 Technology AI User Behavior Product Development Market Trends
  1. AI products require users to change their mindset. Instead of expecting a perfect answer right away, users learn to work with AI to get better results over time.
  2. AI doesn't just replace existing tasks; it creates new opportunities. Users can now ask AI to do many things that were difficult or time-consuming before.
  3. Using AI tools gives valuable insights into user behavior. Users feel more comfortable asking simple or repetitive questions that they wouldn't ask a human, providing helpful data for improving the product.

There's more to LLMs than chat

233 implied HN points • 15 Feb 24
🕹 Technology AI UX Design Information Retrieval Human-computer interaction
  1. Chat interfaces have limitations, and using LLMs in more diverse ways beyond chat is essential for product innovation.
  2. Chat-based interactions lack the expression of uncertainty, unlike other search-based approaches, which impacts user trust in the information provided by LLMs.
  3. LLMs can be utilized to proactively surface information relevant to users, showing that chat isn't always the most effective approach for certain interactions.

OpenAI is too cheap to beat

386 HN points • 12 Oct 23
  1. Data is crucial for giant companies like OpenAI.
  2. Infrastructure scalability is a significant advantage for OpenAI.
  3. The ability of major LLM providers like OpenAI to serve models at extreme economies of scale gives them a major advantage.

A tornado of AI news

70 implied HN points • 01 Mar 24
🕹 Technology Machine Learning Models Ethics Open Source
  1. OpenAI, Google, Meta AI, and others have been making significant advancements in AI with new models like Sora, Gemini 1.5 Pro, and Gemma.
  2. Issues with model alignment and fast-paced shipping practices can lead to controversies and challenges in the AI landscape.
  3. Exploration of long-context capabilities in AI models like Gemini and considerations for multi-modality and open-source development are shaping the future of AI research.

How to Optimize Retrieval-Augmented Generation

140 implied HN points • 07 Sep 23
  1. Retrieval-augmented generation (RAG) combines documents to prompt LLMs in answering queries.
  2. Techniques like Hypothetical Document Embedding and text segmentation can enhance RAG applications.
  3. Custom ranking functions can boost performance by refining the relevance of retrieved documents.

RLHF and LLM evaluations

93 implied HN points • 14 Sep 23
  1. LLMs are a key application of reinforcement learning, especially with human feedback.
  2. RL with computational feedback is a more scalable technique, useful for evaluating code generation models.
  3. Using GPT-4 as a judge has challenges due to positional bias, requiring nuanced benchmarks for evaluation.

MemGPT: Memory Management for LLMs

70 implied HN points • 19 Oct 23
  1. MemGPT is a memory management system for LLMs.
  2. An interview discussed large context windows and the future of conversational AI.
  3. No blog post this week due to a vacation, but an interview video was published.

LoRA, explained

49 HN points • 21 Sep 23
  1. LoRA optimizes model fine-tuning by reducing parameters and improving memory efficiency.
  2. LoRA enables broader access to fine-tuning LLMs by reducing resource requirements.
  3. Techniques like LoRA are crucial for innovation in Large Language Models.

How our Test-of-Time Paper Almost Wasn’t

46 implied HN points • 19 Sep 23
  1. Obstacles in research can turn into the research itself.
  2. Entering new research communities requires learning to be a part of that community.
  3. Building, growing a community, and having a strong team are key for successful research.

A deep dive on Llama Index

46 implied HN points • 23 Aug 23
  1. Llama Index is an open-source project for developers to connect data sources to their LLMs seamlessly.
  2. The project has gained remarkable traction in 2023 and was founded by Jerry Liu.
  3. The podcast episode discusses the evolution of the ML space and where Llama Index is headed.

Rumors of startups’ deaths have been greatly exaggerated

10 HN points • 16 Nov 23
  1. Rumors of startups' deaths have been exaggerated, OpenAI is creating an ecosystem for applications to flourish.
  2. For startups doing basic retrieval or building vector databases, differentiation will be key to surviving.
  3. OpenAI's improvements create more use cases and depth, positioning them as the core infrastructure for AI applications.

One LLM won't rule them all

9 HN points • 02 Nov 23
  1. Rise of specialized LLMs rather than one universal model
  2. ASLMs are designed for specific tasks, cheaper and faster
  3. Focus on making LLMs smaller and more efficient in open-source community

Don't build your application on a single LLM call

5 HN points • 14 Mar 24
🕹 Technology Artificial Intelligence Application development Software Engineering User Experience
  1. Avoid building your application solely on a single Large Language Model (LLM) call. Break down your problem into multiple steps for better results and efficiency.
  2. Long, detailed prompts can confuse even advanced LLMs like GPT-4, leading to issues in instruction following, debugging, and user experience.
  3. Different tasks may require different models, so breaking your application into multiple steps allows you to choose the best tool for each task, improving application quality and reducing latency and cost.

Fine tuning is just synthetic data engineering

6 HN points • 11 Jan 24
  1. Fine tuning involves using synthetic data to train models.
  2. Synthetic data can be generated by powerful models like GPT-4 for efficient fine-tuning.
  3. Data engineering is crucial in fine-tuning for tasks like dataset size, diversity of examples, and model performance.

Why open-source LLMs matter

7 HN points • 26 Oct 23
  1. Open-source LLMs can be valuable by allowing community oversight and understanding of a model's biases.
  2. Re-creation of models from open-source LLMs may be challenging due to the high costs and infrastructure requirements.
  3. Open-source LLMs can excel in specialization, offering a path forward for OSS through smaller, more focused models.

Gorilla: An LLM for Massive APIs

7 HN points • 28 Sep 23
  1. Fine-tuning with retrieval in mind improves model performance.
  2. Retrieval is crucial for keeping API documentation fresh.
  3. Fine-tuning a model for massive APIs involves nuances.

Building your first LLM-powered application

8 HN points • 17 Aug 23
  1. LLMs are powerful tools that require the right balance in how they are used.
  2. You don't always need to fine-tune a model; data is key in customizing usage.
  3. Experiment with different parameters like prompt customization and segmentation for improved performance.

An introduction to evaluating LLMs

4 HN points • 25 Jan 24
  1. LLMs have different strengths for different tasks - such as analysis, code generation, or general knowledge.
  2. Human evaluations are crucial for understanding model quality, considering human needs.
  3. LLM-specific evaluation techniques like MMLU and MT-Bench focus on a wide range of tasks and conversational abilities.

Open-source LLMs shouldn't try to win

6 HN points • 05 Oct 23
  1. Open-source LLMs face challenges competing with proprietary models like GPT and Claude due to significant advantages.
  2. Instead of trying to match the quality of proprietary models, open-source LLMs can focus on becoming smaller, cheaper, and more customizable.
  3. The success of open-source LLMs depends on specializing in certain tasks, increasing efficiency, and maintaining quality at a smaller scale.

Building on quicksand

3 HN points • 07 Mar 24
🕹 Technology AI App Development Machine Learning Innovation Software
  1. Stay updated with AI news, but avoid diving too deep into becoming an expert. Focus on relevance to your product.
  2. Design applications for flexibility to adapt to evolving technology. Consider configurable components for easier updates.
  3. Identify what aspects of your project are core and non-negotiable, versus what can be changed. Be clear on priorities to navigate the pace of innovation.

2024 AI Predictions

4 HN points • 04 Jan 24
  1. OpenAI's progress might slow down due to corporate drama but cost-cutting will continue
  2. Open-source LLMs will face challenges against commercial LLMs
  3. Predictions include reduced investment in AI companies in 2024 and advancements in per-token fine-tuning services

The Easiest Part of LLM Applications is the LLM

6 HN points • 24 Aug 23
  1. LLM applications involve more than just the model, including deploying, managing, and optimizing cloud resources.
  2. Tracking application performance with LLMs is crucial to ensuring accurate outputs and avoiding errors.
  3. Managing access control, budgeting costs, and handling credentials are significant considerations for LLM applications.

Prospecting in the AI Gold Rush

3 HN points • 18 Jan 24
  1. Consider building tools for people using AI instead of just using AI to build new applications.
  2. In the AI space, focus on innovating new applications rather than supplying tools.
  3. When working with AI, aim to find solutions that can significantly benefit enterprises for a higher chance of success.

Should you fine-tune a model?

4 HN points • 31 Aug 23
  1. Fine-tune a model when it needs to learn a skill that can't be explained with a few examples.
  2. Off-the-shelf models are good for synthesizing specific information and generalized skills.
  3. Provide the right information for zero-shot learning in applications like data analysis and text generation.

OpenAI will solve its privacy problems before you

3 HN points • 09 Nov 23
  1. OpenAI is investing in solving privacy problems and will likely address them before individual users do.
  2. Using open-source models for privacy reasons is complex, expensive, and may not be practical due to advancements by major model providers like OpenAI.
  3. Cloud providers like OpenAI, Google, and others are working on privacy solutions, making off-the-shelf secure LLMs more accessible in the near future.