Category: Daily Reading List

Did you have a good start to your week? After writing a few thousand words this weekend about Cloud Run, I’m hunting for the next tech to mess around with. Suggestions?

[blog] Embeddings: How to select the right one? Here’s a good treatment of an important concept in the real of large language models.

[article] Measuring developers’ jobs-to-be-done. This story reviews a recent Google paper that explored “developer goals” that help internal teams optimize the dev experience.

[blog] A Complete Guide to Leveraging Langchain.js and Google Cloud Functions for AI-Powered Applications. Fairly robust example here from Tanya that looks at a few dimensions of calling LLMs from a serverless environment via frameworks like LangChain.

[article] Why Generalists Own the Future. Specialists or generalists? It’s a common debate. Dan argues that generalists are well positioned in this dynamic future because they will adapt faster than those with specific expertise.

[blog] New Gemini model in LangChain4j. If you want a simple model-as-a-service experience with Gemini, the Google AI edition is a great place to start. If you’re looking for more advanced AI/ML features to go with it, Google Cloud’s Vertex AI is an ideal starting point. For the former, you can now use LangChain4j.

[article] 4 Pillars of Innovation Every Organization Needs. There are some good pieces of advice here, including some “avoid this” direction which can be valuable.

[paper] Multimodal Web Navigation with Instruction-Finetuned Foundation Models. Dense subject (to me), but there’s clearly value in getting better at autonomous web navigation.

[blog] Can Postgres replace Redis as a cache? Answer, not really. Use a proper caching tool when performance matters.

[blog] The new wave: Top 10 emerging .NET content creators. Do a regular (annual?) review of your feeds/follows to see if it’s gotten stale. Find new folks who are publishing!

[blog] Five Lessons Learned From a Lifetime of Platform-as-a-Product. Good lessons from someone who has lived it.

[blog] The insidious problem of configuration sprawl. If you’ve got all your system, application, and infrastructure configurations in a single place … you’re a unicorn. I’d like to meet you. Most folks have configs all over the place, as Brian talks about here.

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It was officially a 4-day workweek, but felt like a regular week. Lots going on, and plenty of things to do. But I greatly prefer that to the alternative! Have a great weekend, y’all.

[blog] Serving Stable Diffusion with RayServe on GKE Autopilot. How would you make this text-to-image model available to other apps in your environment? William gives us a step by step for getting it going on Kubernetes.

[blog] Coaching Feedback. I’m familiar with the SHARE model for giving feedback, but don’t always remember to use it. This is a good reminder to break it out more often.

[blog] Google named a leader in the Forrester Wave: AI/ML Platforms, Q3 2024. You like us, you really like us. It’s cool to see Google as the only hyperscale cloud in the leader section.

[blog] Getting 🍨 Ice Cream 🍦 Recommendations at Scale with Gemini, Embeddings, and Vector Search. Alok really likes ice cream. He’s also great at AI/ML and helps us understand the role of embeddings in creating a recommendation engine.

[article] Cycle Time. Most of you are trying to shrink the time it takes to go from idea to working software in production. But what activity starts the “cycle time” clock? And when is the software considered “shipped”?

[blog] Securing Generative AI: Defending Against Prompt Injection. I thought this was good advice for a problem most of us hadn’t even thought much about yet.

[guide] Enterprise application with Oracle Database on Compute Engine. There’s more going on than just AI stuff. Here’s a good new guide on hosting a highly available app that depends on Oracle databases. All on VMs.

[blog] Gemma explained: PaliGemma architecture. This is an open vision-language model that produces a text response from image or text input.

[article] InfoQ AI, ML and Data Engineering Trends Report – September 2024. What’s “late majority” versus “early adopters” in this fast moving space? Here’s one lens on it.

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It was a good day. I had productive meetings, one epiphany, and a chance to write. In the reading list below, you’ll find some tech deep dives, but also a few pieces that’ll help you with strategic thinking.

[blog] Using Node-based pricing on GKE Autopilot. Fully managed Kubernetes is a good deal. William talks about the couple of ways (pod based, node based) to pay, and how the new Custom Compute Class gives you a very flexible way to define workload priorities.

[article] TikTok Releases Tool to Improve Monorepo Performance. Google famously has a monorepo, but there’s work to be done to make it usable for every developer. This article explores a new tool that helps devs pull subsets of files.

[blog] Building LLMs from the Ground Up: A 3-hour Coding Workshop. Folks are learning AI from lots of places, including YouTube. Great video here from Sebastian.

[blog] Looks Matter (When It Comes to Software Products). Good product design isn’t a nice-to-have; it’s critical for long term success.

[blog] Best practices for cost-efficient Kafka clusters. Lots of details here, whether you’re self-hosting or using a managed environment for event stream processing.

[blog] AI Security frameworks in a nutshell — Part 1. Sita does a great job looking into the industry and government-led frameworks that matter for security folks. Also check out part 2.

[blog] From Idea to Reality: Building the Instant Web with Gemini (Part 1). How can you go from wireframe to website with the help of an LLM? Thu Ya has a good example of the iterative process.

[blog] Why “AI” projects fail. The amusing rant here claims that AI projects fail because folks “do AI” to avoid the harder work of identifying and fixing real problems.

[blog] Telemetry in Go 1.23 and beyond. I like the transparency and insights provided by the Go team related to user-provided telemetry about Go usage.

[blog] How to consistently output JSON with the Gemini API using controlled generation. Gemini was among the first LLMs to offered structured output in JSON, and this post explains why it matters, and how to use it.

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I can’t come up with any interesting intro today, so I asked Gemini for a joke about open source software. “Why did the open source software go to therapy? It had to many unresolved issues.” AI isn’t taking my job any time soon.

[article] New LLM Pre-training and Post-training Paradigms. What sorts of pre-training and post-training is available to LLMs? And how do leading open models employ (or not employ) these approaches? Great writeup.

[guide] Select a managed container runtime environment. Which type of managed compute service makes sense for your next app? This new architecture guide may help you decide.

[article] What’s Behind Elastic’s Unexpected Return to Open Source? More on this somewhat-surprising move to make Elasticsearch more open again.

[blog] What are the most common bugs in LLM-generated code? It’s good to see and digest this. And it reinforces my belief that you should know how to code before depending too heavily on these AI assisted tools.

[article] Why We Shouldn’t Romanticize Failure. Ah, maybe we shouldn’t be so quick to crave a “fail fast” and “celebrate failure” culture? It sounds like we over-estimate our resilience.

[blog] BigQuery and Anthropic’s Claude: A powerful combination for data-driven insights. There’s some nice integration here between a great LLM and a terrific analytics platform.

[article] What to Do When You Know More Than Your Boss. You should know more than your boss in many areas. This is an article about knowledge sharing.

[blog]. A retryable JUnit 5 extension for flaky tests. If you’re starting to invoke LLMs in your apps, you might want to rethink your testing strategy. Guillaume wanted retry-able tests to account for non-deterministic responses.

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I had a good 3-day weekend and will now struggle all week to remember what day it actually is. Today’s reading list offers some controversy (“founder mode!”), survey data (“Python users!”), and a little intrigue (“web3 heists!”).

[blog] Measuring meaningful availability / uptime of Wise. How does availability differ from uptime, and how does this fintech company look at reporting those values? Educational read.

[blog] Founder Mode. This one had the socials buzzing over the weekend, with lots of contrasting takes. I liked it. “Manager mode” versus “founder mode” can even apply within existing companies; you see the creators of teams or divisions choose either path.

[blog] Mastering Controlled Generation with Gemini 1.5: Schema Adherence for Developers. You want a blueprint for how responses come back from an LLM? Gemini has offered JSON mode for a while, and this lets you define a schema for the LLM response.

[blog] Svelte adoption guide: Overview, examples, and alternatives. I hear good things about this frontend framework, and this is a big post with tons of details.

[blog] Your ultimate guide to the latest in generative AI on Vertex AI. You don’t need to follow every announcement in tech. I mean, you’re reading this daily post, so you’re probably fairly up to date. But, I like these sorts of recap blogs that give you a single place to catch up.

[site] Python Developers Survey 2023 Results. Some fresh survey results here which convey insights into developer choices in frameworks, tools, clouds, and more.

[blog] DeFied Expectations — Examining Web3 Heists. I know there are books about this topic, but wow. There’s a lot of money and a lot of creative attacks at play.

[paper] Generative Verifiers: Reward Modeling as Next-Token Prediction. New paper from DeepMind that looks at training models as verifiers of LLM responses.

[blog] Flutter Vs React Native : Performance Benchmarks you can’t miss ! 🔥⚡️ [Part -1]. I haven’t seen many benchmarks like this, and it’s useful to see where each framework lines up on head-to-head comparisons.

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If you’re in the US, there’s a three-day weekend in front of you. Enjoy it!

[article] How to Craft a Memorable Message, According to Science. We forget most of what we hear or come across. Can you ensure folks don’t forget what you tell them? Read this.

[blog] Debate over “open source AI” term brings new push to formalize definition. It’s good to have an agreed-upon definition of open source AI, as folks are using that term to refer to models that don’t fit the traditional open source definition.

[article] What a day in the life of a Technical Writer in the energy industry looks like — Guest post by Bonnie Denham. I see our tech writers in action every day, but this is a good look at the types of activities the role might entail.

[blog] Feature Flags are more than just Toggles. There are many ways to implement feature flags, and Derek encourages us to think more broadly than just conditional statements in code.

[blog] Google Cloud launches Memorystore for Valkey, a 100% open-source key-value service. Life finds a way. When previously open products get license changes, new options emerge. Valkey is a solid alternative to Redis, and I’m glad we’re offering it as a managed service.

[blog] Top Five Platform Engineering Books for 2024. Getting your team to think about a platform engineering approach? These books can put you in the right frame of mind.

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Big reading list today! It includes some tech dives, inspiring text-to-image AI examples, and some strong opinions about JavaScript frameworks and software estimation.

[blog] What is the Kubernetes “Claim” model? File this under “things you don’t HAVE to know, but are useful nuggets to store away.” Brian provides context into what these “requests” into Kubernetes mean.

[blog] Long context prompting tips. Small tips from Anthropic, but potentially very impactful ones.

[article] How Platform Engineering Enables the 10,000-Dev Workforce. Big post, but lots of good coverage of this topic. Why do platform engineering? How do you measure impact?

[article] Generative AI coding startup Magic lands $320M investment from Eric Schmidt, Atlassian and others. AI coding tools are so hot right now! Magic raised a ton. And announced they are training models on Google Cloud.

[blog] A Java Language Cumulative Feature Rollup. If you haven’t checked out Java since version 8, you’ll like this recap of everything important that’s happened since then.

[blog] Gemma explained: RecurrentGemma architecture. This isn’t the “standard” LLM architecture, and is worth reading about.

[blog] Building Out 🍨 Ice Cream 🍦 Product Assets at Scale with Gemini. Come up with creative product descriptions and summarize reviews with AI. Fun demo.

[article] Is Your Organizational Transformation Veering Off Course? How do you navigate those turning points to get things back on track? There’s good advice here.

[blog] Get more photorealistic with Imagen 3. I’m still wow-ed by AI-powered image generation, especially those of living creatures. This post shows some remarkable results.

[blog] A developer’s guide to getting started with Imagen 3 on Vertex AI. Here’s some useful advice on prompting text to image models like Imagen 3.

[article] Developers Rail Against JavaScript ‘Merchants of Complexity’. The use of frameworks is a topic that can spark wildly different opinions. This piece shares skepticism of the value.

[blog] Software estimates have never worked and never will. It’s always been “confident guessing”, especially for any estimate longer than 2 days. DHH says to look at “budgets” instead.

[blog] Elasticsearch is Open Source, Again. I think this is the first of the “switched our open license to closed” vendors to actually go back to something more open. Kudos!

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I paid for yesterday’s light-meeting day with a heavy-meeting day today. Well-played, calendar gods. But I also read some great content, and even had time for some quick demos about AI-generated data insights and attached volumes on a serverless app.

[blog] New in Gemini: Custom Gems and improved image generation with Imagen 3. Here’s a good update for those that want personalized assistants, or some premium image generation.

[blog] Gemini Chat App. Simon used Claude to write a small app that uses our latest Gemini 1.5 model versions. He also opines that people who don’t see value in using AI assistance for programming are missing something.

[article] Why Cynics Are Less Likely to Succeed. It’s not hard to be cynical, but operating in a mode of trust and cooperation is not just good for your mental well-being, it’s better for your career.

[blog] What is the Open Source Alternative to CockroachDB? When license changes happen, other vendors/projects jump in. Denis at Yugabyte offers up a case for using their database as a drop-in replacement.

[blog] Building an AI-Powered CLI with Golang and Google Gemini. I’m definitely seeing more organic usage of the Gemini models. I guess that’s what happens when “quality models” meets “wildly generous free tier.”

[blog] Managing Angular. This is a high level view from the product lead for the popular JavaScript framework. OSS management is quite the job, whether you’re solo or working inside a big tech firm.

[article] Speak, Code, Deploy: What if voice was your primary tool for coding? I dunno. I’m hoping speech-to-text and chatbots are transient interfaces with AI. At least for the masses that don’t need that for accessibility reasons. I personally don’t want to talk to my computer, or be stuck “chatting” to get my work done.

[blog] Get started with the new generally available features of Gemini in BigQuery. *This* is how I want my AI. Melted into the products I use. These are great features for smarter analytics.

[article] Applying AI to the SDLC: New Ideas and Gotchas! – Leveraging AI to Improve Software Engineering. Good talk and transcript for those considering a more delivery-wide view of AI assistance in their software teams.

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I had a very light meeting day today, which messed with my head. But, it was great to answer all my email, write a blog post, do some research, and work on upcoming presentations.

[blog] Routines and habit stacking. Tom looks at incorporating goals into current routines, and piggybacking on existing success.

[blog] Level up your codebase with Gemini’s long context window in Vertex AI. I love this example from Karl. He shows us exactly how to take a large codebase and use Gemini to send prompts like “provide a getting started guide” and “implement this feature.”

[article] Does Market Share Still Matter? Do “market leaders” have the most efficiency, market power, and quality? Or do highly digital firms have similar profitability to the market leaders? Interesting research.

[article] Profitable on day one! What does it even mean to be “profitable? Jason encourages us to use the term correctly.

[blog] A Year of Project IDX. If you haven’t checked this out, at least give it a scan. IDX is an interesting developer environment, and I’ve used it to build a few apps.

[blog] What conditions make developers thrive most? This looks at recent research about where devs don’t just perform, but thrive. Four key dimensions come into play.

[blog] Friction Logs. This is about the process of using products, recording the experience and papercuts that come with it, and sending that feedback to those who fix it.

[article] Why AI can’t spell ‘strawberry’. Such an interesting problem! I tried this scenario with the latest Gemini Flash models we released today, and it did indeed answer correctly.

[blog] How DoorDash is pushing experimentation boundaries with interleaving designs. Sophisticated stuff, but looks like a useful strategy for getting better signals earlier.

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I respected summer this weekend by being outside and rejecting any retail efforts to make me buy Halloween decorations or drink pumpkin spice drinks. Stay strong!

[article] Strangler Fig. I’ll be honest with you. I’ve referred to this design pattern a few times, without having any idea why it was called that. Now I do, in this updated post from Martin.

[article] Google Brings Serverless Inference To Cloud Run Based On Nvidia GPU. Jani offers up a solid look at the “what” and “why” of offering GPUs in our hyper-scalable cloud service.

[blog] How to Design Twitter (X) in a System Design Interview? I’ve never asked the question in an interview, but I liked the step by step approach offered here.

[blog] Gemini Bounding Box Visualization. Simon uses Claude to write some code against Gemini APIs that produce a bounding box around specified objects. Cool!

[blog] Unlocking 7B+ language models in your browser: A deep dive with Google AI Edge’s MediaPipe. Take a look at this service for on-device ML solutions, as it now can handle even larger models.

[paper] Measuring Developer Goals. This new paper highlights 30 developers goals that we at Google track to help improve the developer experience for our engineers.

[blog] Manager Antipatterns. Ted calls out things that most of us in management roles have done (or are doing), and should actively try and rectify.

[article] Valkey Is a Different Kind of Fork. The team behind this Redis fork has version 8.0 coming soon. Redis itself also shipped a major version 8.

[blog] Software Licensing Changes and Their Impact on Financial Outcomes. Related to the Redis topic. Rachel at Redmonk looks at whether companies that did OSS license changes found later success.

[blog] Less Is More: Principles for Simple Comments. Simple post for a simple topic. Write self-contained comments that aim for simplicity.

[blog] Improve Your Next Experiment by Learning Better Proxy Metrics From Past Experiments. From the Netflix team, this looks at how to think about A/B tests and the relationship between short term and long term metrics.

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