Felipe Tonello

The policies being driven forward by Thierry Breton around the EU AI Act have been utmost damaging for the European tech industry, and they need to be revised as soon as possible. We need to better balance driving European innovation through data collection and modeling with conflicting needs such as data protection and safety. We need to ensure we can turn these developments into businesses that can be the pillar of the next generation of companies that provide productivity growth in Europe. We have no other choice - either we do this here, or we will have to take over products developed in the US and in China, as it already happened with search engines, cell phones, and social networks. We wouldn't want to let this happen with AI applications in robotics or digital assistants. #euacc #euaiact #aiact

9

sharpening writing skill is one of killer skill!

2

Hey everyone, I am an investor and advisor to Katanemo.... They are reinventing the infrastructure primitives for GenAI and launched Arch - https://lnkd.in/gCrbbkEV - an intelligent prompt gateway. Give the project a look on Github, and star the project if it resonates

88

3 Comments

Go Tip #6: Using Generics for Better Type Safety in Go Generics in Go provide an effective way to write flexible yet type-safe code. By using type constraints and ensuring that only pointers to valid types are passed to our unmarshaling function, we eliminate a class of runtime errors and make our code more maintainable. https://lnkd.in/dRvuVBJe #GoLang #Programming #SoftwareEngineering #CodeQuality #GoDeveloper #CleanCode

7

What does it feel like to be an engineering manager at Amazon: Since the RTO announcement it became popular to write shit about Amazon, but it's still one of the best companies around the world. A few weeks ago, Gilad Naor shared with the 'Leading Developers' readers 3 lessons from his time as an engineering manager at Amazon. Here is the first one, read the full article here: https://lnkd.in/dHBxv2Ha 𝟭. 𝗦𝗼𝗹𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘄𝗶𝘁𝗵 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 My first meeting at Amazon was baffling. I entered a room with about a dozen Software Development Managers (SDMs) and Product Managers (PMs). Everyone sat in silence and looked at their laptops or the printed papers they had before them. It was my second day at Amazon, and I picked up one of the extra sheets and started reading. After those first ten awkward minutes, everyone finished reading and the meeting began. Why do Amazon meetings always start this way? This is a specific example of how Amazon approaches problem-solving. It is the "Good Intentions Don't Matter" approach. In a famous internal video, Jeff Bezos talks about why good intentions are irrelevant. When things don't work, it does not matter what people wanted. All that matters is fixing the problems with mechanisms. For example, many busy managers come to meetings without first reading the prep material. They all intended to read the 6-Pager before the meeting. They just got sidetracked by the latest emergency. Scolding the managers won't solve the problem. A mechanism will. This is why all Amazon meetings start with a study period. The remaining meeting time is then spent on high-quality decision-making. Meta solves the same problem by creating cultural norms. I can't count the number of times that I heard a senior leader cut off the presenter: "Stop, assume that everyone here already read the slides. Let's get to the point." This is a culture-first approach. I have seen this work well in some teams, and not work at all in other teams. At Amazon, the solution is part of the company's "operating system". ------------------------- Anton here - by now everyone is familiar with the 6-pager concept, but that "operating system" thinking can be used almost anywhere. Think about something annoying that your developers do, and how can you solve it. For example, I remember being annoyed that people don’t update the Jira tickets. Finally, after requesting multiple times, now we just take 20 seconds from the standup to update them.

56

8 Comments

ModernBERT is an open-source embedding model which represents a significant leap in encoder-only transformer architecture merging: > Long context (8k tokens) > Local-global attention for efficiency > Up-to-date code and text training data (2 trillion tokens) > FlashAttention & Unpadding to push performance on consumer and server GPUs > High retrieval and classification accuracy that sets new benchmarks across GLUE, BEIR, code tasks, and more. Learn more about this exciting new embedding model here: https://lnkd.in/en5ud9VE

2

At the risk of starting a flame war: what's the benefit of using Gradle over Maven? Almost every Gradle project I've downloaded fails to start without tinkering, whereas Maven projects generally just work. Gradle also doesn't seem to support the latest Java. What am I missing?

27

11 Comments

On April 18th, just last week, Meta unveiled Llama 3, lauded as "the most capable openly available LLM to date". This remarkable achievement by the Meta team comes hot on the heels of Llama 2's release last July. Notably, the new models surpass their predecessors and competing offerings from other providers in terms of performance. Meta's commitment to open sourcing these powerful models while prioritizing model safety and responsible usage is commendable. (https://lnkd.in/ghv3RJRz) Yesterday, Microsoft introduced Phi-3 Mini, a member of the Phi-3 family of models, promoted as "the most capable and cost-effective small language models available". According to Microsoft, the key to achieve high performance in such a small package lies in the quality of the training data. The model has been open sourced and is now available on Ollama and Hugging Face. (https://lnkd.in/gKgSmrBn)   These compact yet powerful models serve as invaluable tools for creators eager to bring their visions to life or simply experiment with cutting-edge technology, particularly for those with limited access to resources.   Shortly after the release of Llama 3, reports began surfacing of enthusiasts running the 8B model on a Raspberry Pi 5 equipped with just 8GB of RAM—a modest $80 single-board computer. (https://lnkd.in/gbeQPhQ2) While the performance understandably reflects the limitations of such a platform, the fact that the model runs and generates an output is nothing short of magical. Similarly, the Phi-3 Mini model can run on ubiquitous devices such as smartphones.   Reflecting on my own journey, I recall the pivotal role that early access to a low-cost computer played in shaping my passion for coding. The gift of an 8-bit Commodore 64 home computer ignited my curiosity at a young age. Later, with the guidance of an outstanding Computer Science teaching staff at my high school IISS Marconi-Hack Bari, I embarked on ambitious projects like a networked multiplayer version of Battleship written in Turbo Pascal and 8086 Assembly, complete with custom sprites and lo-res graphics. These formative experiences paved the way for my career, eventually leading me to various engineering and leadership roles.   It all began with a simple, low-cost, modestly capable computer used for experimentation—a gateway to discovering my life's passion.   I encourage educators and parents alike to consider setting up similar low-cost experimentation environments. By introducing younger generations to open-source AI technologies, we can nurture their creativity and help them uncover their true passions. It's not just a weekend project—it's an investment in their future and the future of innovation and discovery. #education #youth #AI #LLM #future #innovation

24

I ran across this post in the Changelog newsletter: https://lnkd.in/eCdjVekq - I like the name slop for unwanted AI-generated content. I love LLMs. They are part of my daily workflow, and I've been exploring them more seriously while on my sabbatical. I wouldn't misrepresent generated content without critical review as my own, but the web seems to be filled with more generated garbage (or 'slop')—content that looks fine at first but isn't accurate or helpful. LLMs (and other generative AI) should help us to make things of higher quality, not just churn out slop more quickly.

10

Nice posts about loading the entire Blueky's social network in memory https://buff.ly/3BLVUrg and some optimizations https://buff.ly/4iNDR4H

1

This is an excellent blog comparing the streaming ingest of row-level operation comparison among the LakeHouse formats. https://lnkd.in/dsCN66_5

1

Performance is key! As we cannot share the detailed work with (former) customers of Rockset (acquired by OpenAI), we decided to implement the Rockbench benchmark to check how streaming ingest between CrateDB and Rockset (acquired by OpenAI) compares. The results are very insightful. We see: ✅ CrateDB achieves 6-9x lower latencies than Rockset for streaming ingest. ✅ CrateDB outperforms Rockset on the same hardware while saving about 20% costs. ✅ When volumes increase, the latency increased linearly in Rockset, while remaining mostly flat in CrateDB. As a plus: 1️⃣ Achieve full high availability due to the distributed nature of CrateDB (benchmark results are achieved including replication in CrateDB) 2️⃣ Achieve higher throughput on the same amount of CPUs as Rockset (at 50% of memory needs) 3️⃣ Full open-source solution that prevents you from market shifts On side note: If you are thinking to switch to Elastic/OpenSearch, this benchmark has been created to compare Rockset vs. Elastic, so CrateDB is also faster than Elastic for streaming ingest cases! Read the full comparison here: https://lnkd.in/d_nEMDkx

29

1 Comment

Anthropic's latest Claude AI model is taking human-AI interaction to a whole new level! 🖥️🤖 The new Claude 3.5 Sonnet can now use computers just like a human would - by looking at the screen, moving the cursor, clicking buttons, and typing text. This is a major leap forward in AI capabilities. Key features: Operates based on screenshots, emulating human computer use Can perform complex multi-step tasks across different applications Self-corrects and retries when encountering obstacles2 Accessible via API for developers to build with Why it's game-changing: This opens up incredible possibilities for AI assistance in data entry, form filling, software testing, and automating repetitive computer tasks13. Imagine having an AI assistant that can seamlessly work alongside you, handling tedious computer work while you focus on higher-level tasks. While still in beta, this technology demonstrates how AI is rapidly advancing toward more human-like interaction with digital interfaces. As it develops further, it could revolutionize how we work with computers and boost productivity across industries. The future of human-AI collaboration just got a lot more exciting! What do you think about this new capability? #AIInnovation #FutureOfWork Here is a video about it: https://lnkd.in/gh8aNYEu

6

<rant subject = #streaming and #queues> Streams have stronger ordering guarantees than message queues. Generally this means that all messages within a partition are read in the same order. This is highly useful when you need an ordered record of events that happened. As a consequence, streaming systems don't have individual message acknowledgement as it would violate ordering guarantees. Message queues have per-message acknowledgement, which means they have less strict ordering guarantees. One hybrid approach which is quite cool is "key-based subscriptions" in Apache Pulsar and also Confluent's Parallel Consumer for #apachekafka which guarantee order for all messages with the same key while allowing you to individually acknowledge messages. The tradeoff with individual message ack's is persistence (disk or memory)—you need to keep track of which messages have been acknowledged and which ones are still inflight. Pulsar uses BookKeeper to persist "subscriptions." Parallel Consumer stores them in the user-defined metadata in the consumer offset topic. There's an in-flight KIP for "Queues on Kafka" (KIP-932) but if I were the Grumpy Maintainer of Kafka, I would say that it's unnecessary given that it's already possible to implement queue semantics on top of Kafka in the client side (see: Parallel Consumer). Why add additional code bloat on the broker side when the features are already accessible? </rant>

4

Some folks at Anthropic announced the open source protocol -- and some sdks -- they use for communicating between LLM apps and servers. They use it for Claude desktop. As a high level overview: - All communications local or remote are done with JSON RPC 2.0. - Messages are broken into 3 familiar categories: 1. Requests 2. Results 3. Notifications - one way messages - Defines a lifecycle for interactions between client and server. On the client side it standardizes methods of adding context to prompts so you can add connections to basically anything like file servers, databases, Slack, Git repos, local files and even local processes i.e. screen sharing, etc. On the server side you essential tell the client what is available to them, like resources i.e. databases or services, tools, prompt templates, etc. This is huge for anyone that has messed with LLMs and wondered, "How can I hook up x to this"? -- and no, I don't mean the platform formerly known as Twitter. Or has used LangChain and found it cumbersome for just hooking up an LLM to a tool. https://lnkd.in/gByFWRZD -- the announcement https://lnkd.in/gGmm_pw6 -- the specification site They have quite a few example servers for hooking up Github, Slack, and Google Drive to name a few.

1

I’m interested in some advanced (or at least not well documented AFAICT) topics in autoscaling. What should I read/watch? Who should I talk to? • Designing autoscaling to be robust to metrics incidents • Avoiding false positive scaling actions (e.g., scaling when the bottleneck is not CPU or RAM) • System aware component autoscaling (avoiding overwhelming dependencies, avoiding dependencies scaling down during incidents and causing thundering herds, etc).

5

2 Comments

I just realized: we should start saying "build memoization" rather than "build caching." We aren't really talking about least-recently-used or time-to-live algorithms, we're talking about "don't bother recompiling this piece if none of its inputs changed." (It has been puzzling me that some people are so deeply fearful of "build cache" as a concept, and honestly I think it's mostly because most build systems have non-hermetic state scattered all over the place. Maybe a slightly more precise term for the type of caching we're concerned with would clarify the semantics in question.)

83

9 Comments

First blog post in a long time! I started writing a post ~2 years ago on adventures counting 130M U.S. buildings by zipcode and finally circled back to write it up properly. Everybody is a winner really, but DuckDB, Ibis, Apache Arrow, and GeoParquet were absolutely essential for getting the data into and out of all of the various engines. Give it a read and let me know what I missed! https://lnkd.in/evgVe3vD

61

2 Comments