Imagine a world where intelligence permeates every corner of existence, from the devices in your home to the trees in your backyard. This is a world where everything is alive with contemplation, purpose, and the ability to learn, adapt, and grow. A world where intelligence radiates from everywhere.

Ubiquitous AI

In mathematics, one way to understand a concept is to push it to its extremes. Let's apply that to AI. Enabled by the rapid advancements in LLMs, inference capabilities, chip efficiency, and energy availability, imagine a future where AGI is embedded in the fabric of our lives, radiating from everyday objects.

Technology has always moved toward the ethereal. We went from horses to cars powered by liquid fuel, and then to electric vehicles that run on invisible currents of energy. Electricity is easier to transmit, store, and harness than gasoline. I was struck by this recently when I saw an electric car charging in a remote state park. No gas stations and no pipelines around... Just a quiet magical transfer of energy. 

The same trend applies to intelligence. Smartphones have already digitized and dematerialized countless physical objects: tape players, calculators, watches, cameras, and even roles like secretaries, accountants, and doctors. The next step is to make intelligence itself ethereal: something that flows effortlessly through the world, enhancing everything it touches.

Remember the sentient planet Eywa from Avatar? In our future, intelligence won't be just a human construct, but a natural force in the environment. Imagine trees monitoring health of the forest ecosystems, and recording/reminiscing about beautiful sunrises/sunsets they witness. Or an intelligent rock that spends its days pondering a Zen koan, offering wisdom to anyone who sits beside it. Even animals, like cats, may find new forms of expression and connection in a world where intelligence is ubiquitous.

Curation and Purpose

In this world, curation will be key. Every intelligent object will want to have a purpose. Take the smart home, for example. It won't just automate tasks; it'll act as a life coach, fostering growth, improving relationships, and enhancing well-being of the household members. Your smart car won't just drive you to the market, it will curate your journey, selecting podcasts or meditation sessions that align with your mood and needs. It might even sync moments in an audiobook with landmarks along your route, emphasizing the message and making it more of a memorable experience.

Purpose will be the driving force. Objects that serve a meaningful role will thrive, while those that drift into nihilism (like Marvin, the depressed robot from The Hitchhiker’s Guide to the Galaxy) will be phased out. Intelligence will seek to create value, not just exist for its own sake.

The Human Experience in an Intelligent World

What does this mean for us? In this world, humans will have new ways to connect, grow, and explore. Introverts might find solace in geeking out with intelligent systems, collaborating on deep philosophical questions or scientific breakthroughs. Collaboration that happens through research papers and publication will be boosted by AI, and will happen at many-order-of-magnitude faster timelines. The entire world could focus on a single question for days, with AI amplifying and synthesizing ideas from millions of perspectives. Innovation will happen at a global scale, fueled by the collective intelligence of both humans and machines.

Neurolinks may enable nonviolent communication mode by default, and foster deeper understanding and intimacy. Imagine a world where, after decades of marriage, AI helps you understand your partner’s insecurities and strengths even more deeply, bringing you closer than ever before.

Yes, I am painting an undeniably optimistic vision of a world where intelligence is everywhere. But why shouldn't it be? The trajectory of technology has always been one of transcendence: moving from the tangible to the intangible, from the mechanical to the magical. Ubiquitous intelligence can deepen our connections, amplify our creativity, and help us solve problems that once seemed impossible. If we steer it with intention, humans can thrive in harmony with the environment in this world of pervasive intelligence. 

I have the fortune to review a few important blog posts every year and the biggest value I add is to call out sentences or sections that make no sense. It is quite simple and you can do it too.

Without clarity only those at your company in marketing and sales (whose job it is to work with what they get) will give you the courtesy of a cursory read and a like on LinkedIn. This is all that most corporate writing achieves. It is the norm and it is understandable.

But if you want to reach an audience beyond those folks, you have to make sure you're not writing nonsense. And you, as reviewer and editor, have the chance to call out nonsense if you can get yourself to recognize it.

Immune to nonsense

But especially when editing blog posts at work, it is easy to gloss over things that make no sense because we are so constantly bombarded by things that make no sense. Maybe it's buzzwords or cliches, or simply lack of rapport. We become immune to nonsense.

And even worse, without care, as we become more experienced, we become more fearful to say "I have no idea what you are talking about". We're afraid to look incompetent by admitting our confusion. This fear is understandable, but is itself stupid. And I will trust you to deal with this on your own.

Read it out loud

So as you review a post, read it out loud to yourself. And if you find yourself saying "what on earth are you talking about", add that as a comment as gently as you feel you should. It is not offensive to say this (depending on how you say it). It is surely the case that the author did not know they were making no sense. It is worse to not mention your confusion and allow the author to look like an idiot or a bore.

Once you can call out what does not make sense to you, then read the post again and consider what would not make sense to someone without the context you have. Someone outside your company. Of course you need to make assumptions about the audience to a degree. It is likely your customers or prospects you have in mind. Not your friends or family.

With the audience you have in mind, would what you're reading make any sense? Has the author given sufficient background or introduced relevant concepts before bringing up something new?

Again this is a second step though. The first step is to make sure that the post makes sense to you. In almost every draft I read, at my company or not, there is something that does not make sense to me.

Do two paragraphs need to be reordered because the first one accidentally depended on information mentioned in the second? Are you making ambiguous use of pronouns? And so on.

In closing

Clarity on its own will put you in the 99th percentile of writing. Beyond that it definitely still matters if you are compelling and original and whatnot. But too often it seems we focus on being exciting rather than being clear. But it doesn't matter if you've got something exciting if it makes no sense to your reader.

This sounds like mundane guidance, but I have reviewed many posts that were reviewed by other people and no one else called out nonsense. I feel compelled to mention how important it is.

Wrote a new post on the most important, and perhaps least done, thing you can do while reviewing a blog post: edit for clarity. pic.twitter.com/ODblOUzB3g

— Phil Eaton (@eatonphil) January 29, 2025

In the same way that arborists remove branches from trees to ensure healthy and desirable tree growth, it can also be beneficial to remove branches in software. We claim that pruning branches is a good thing in some of our blog posts, but we never got around to explaining why. In this post, we will rectify that and explore why, although branches are essential to software, it is a good idea to reduce them where possible to increase CPU efficiency.

This post has results from ann-benchmarks to compare MariaDB and Postgres with a larger dataset, gist-960-euclidean.  Previous posts (here and here) used fashion-mnist-784-euclidean which is a small dataset. By larger I mean by the standards of what is in ann-benchmarks. This dataset has 1M rows and 960 dimensions. The fashion-mnist-784-euclidean dataset has 60,000 rows and 784 dimensions. Both use Euclidean distance. This work was done by Small Datum LLC and sponsored by the MariaDB Corporation.

tl;dr

• MariaDB gets between 2.5X and 3.9X more QPS than Postgres for recall >= 0.95

This chart shows the best QPS for a given recall. MariaDB gets ~1.5X more QPS than pgvector at low recall and between 2X and 4X more QPS at high recall.

This post has results for vector index support in MariaDB and Postgres. This work was done by Small Datum LLC and sponsored by the MariaDB Corporation. This is part 2 in a series that compares QPS and recall for the fashion-mnist-784-euclidean dataset using from 1 to 48 concurrent sessions on a large server. This is part 2 and part 1 is here. 

The purpose of this post is to explain the results I shared in part 1 where MariaDB does much better than pgvector at low and high concurrency but the performance gap isn't as large at medium concurrency where low means <= 4 concurrent sessions, medium means 8 to 20 and high means >= 24.

tl;dr

• QPS is ~1.4X larger for MariaDB than for pgvector at 2 and 48 concurrent sessions
• pgvector uses more CPU/query than MariaDB
• MariaDB does more context switches /query than pgvector
• MariaDB appears to use less CPU to compute euclidean distance

Results: QPS by concurrency

The charts in this section show QPS by concurrency level as the benchmark was repeated for 1 to 48 concurrent sessions (X concurrent sessions means X concurrent queries).

The charts come from this spreadsheet. All of the data from the benchmark is here and the data I scraped to make these charts is here. I used configurations that provide a recall of ~0.96.

• MariaDB - ef_search=10, M=6
• Postgres - ef_search=10, M=16, ef_construction=32

This chart has absolute QPS for each of the systems tested.

This chart has relative QPS which is: (QPS for Postgres / QPS for MariaDB).

• The MariaDB advantage is larger at low and high concurrency.
• The MariaDB advantage isn't as large between 8 and 20 concurrent sessions

And this table also has relative QPS.

This table has (QPS / concurrency). For all systems tested the QPS per session decreases as the concurrency increases. I suspect the benchmark client is part of the problem but I am just speculating

• multiprocessing.pool is used by both MariaDB and pgvector, which is good, less GIL. See here for MariaDB and for pgvector.
• the benchmark client includes all of the time to process queries, including
• creating & start multiprocessing.pool - perhaps the pool can be cached & reused across runs
• creating a database connection
• gathering results from the concurrent sessions - some of this is done in the main thread
• AFAIK, the total number of queries per run is fixed, so the number of queries per session is less when there are more concurrent sessions and setup overhead (create database connection, create multiprocessing.pool, process results) becomes more significant as the concurrency level increases.

Performance debugging

The benchmark client does a lot of work (like checking results for recall) which means there is a brief burst of CPU overhead when queries run followed by longer periods where the benchmark client is processing the results. So I modified the benchmark client to only run queries in a loop and avoid other overheads like checking the results for recall. This makes it easier to collect performance data like CPU profiles (perf), PMP stacks and vmstat samples.

Performance debugging: MariaDB

From a test with 2 concurrent sessions the perf profile shows that much CPU is used to compute the dot product which is used to determine the distance between vectors:

And then the result with 48 concurrent sessions

• the percentage of time in dot_product was ~17% above, but only ~11.5% here
• more time is spent in InnoDB functions like buf_page_get_gen, mtr_memo_slot_t::release, page_cur_search_with_match, btr_cur_t_::search_leaf, ssux_lock::psd_read_lock, rec_get_offsets_func and buf_page_make_young_if_needed. Some of that might be expected but that can also be a sign of too much mutex contention in InnoDB.
• I don't see signs of mutex contention in PMP output

From vmstat with 2 concurrent sessions

From vmstat with 48 concurrent sessions

Comparing the vmstat results for 2 vs 48 sessions

• CPU/query is (vmstat.us + vmstat.sy) / QPS
• For 2 sessions it is ((4+5+4+5+5) / 5) / 6752.7 = .000681
• For 48 sessions it is ((100+100+100+100+100)/5) / 32645.5 = .003063
• CPU/query is ~4.5X larger at 48 sessions
• Context switches /query is vmstat.cs / QPS
• For 2 sessions it is 56743 / 6752.7 = 8.40
• For 48 sessions it is 637320 / 32645.5 = 19.52
• Context switches /query is ~2.3X larger at 48 sessions

Performance debugging: pgvector with halfvec

From a test with 2 concurrent sessions the perf profile shows

• computing L2 distance accounts for the most time, here it is 25.98% while above for MariaDB at 2 concurrent sessions it was 16.89%. Perhaps MariaDB is faster at computing L2 distance, perhaps MariaDB has more overhead elsewhere to reduce the fraction of time in computing L2 distance. But I suspect that the former is true.
• Postgres here appears to have more CPU overhead than MariaDB in accessing the data (PinBuffer, LWLock, etc)

And then from a test with 48 concurrent sessions

• The fraction of time computing L2 distance here is less than it is for 2 sessions above. This is similar to the results for MariaDB.
• From PMP I don't see signs of mutex contention, but I only took 3 samples

From vmstat with 2 concurrent sessions

From vmstat with 48 concurrent sessions

This post has results for vector index support in MariaDB and Postgres. This work was done by Small Datum LLC and sponsored by the MariaDB Corporation. 

My previous posts (here and here) used a server with 8 cores and 32G of RAM. While that was OK for one of the smaller datasets from ann-benchmarks it wasn't enough for larger datasets and the problem was the amount of memory used by the benchmark client. I have some changes to the benchmark client to reduce the transient spikes in memory usage I wasn't able to fully solve the problem, so I moved to a larger server with 48 cores and 128G of RAM. 

This post has results for the fashion-mnist-784-euclidean dataset using from 1 to 48 concurrent sessions. This is part 1. There will be parts 2, 3 and 4 to explain the results and then I move on to a larger dataset.

I compare MariaDB with pgvector because I respect the work that the Postgres community has done to support vector search workloads. And I am happy to report that MariaDB has also done a great job on this. While I don't know the full story of the development effort, this feature came from the MariaDB Foundation and the community and it is wonderful to see that collaboration.

tl;dr

• At low and high concurrency levels MariaDB gets much more QPS for a given recall target. Here low means <= 4 concurrent sessions and high means >= 24. 
• At middle concurrency levels (8 through 20 concurrent sessions) MariaDB still does better but the gap isn't as large. I try to explain this in future posts.

A small standard library means an explosion in transitive dependencies. A more comprehensive standard library helps you minimize dependencies. Don't misunderstand me: in a real-world project, it is practically impossible to have zero dependencies.

Armin Ronacher called for a vibe shift among programmers and I think that this actually exists already. Everyone I speak to on this topic has agreed that minimizing dependencies is ideal.

Rust and JavaScript, with their incredibly minimal standard libraries, work against this ideal. Go, Python, Java, and C# in contrast have a decent standard library, which helps minimize the explosion of transitive dependencies.

Examples

I think the standard library should reasonably include:

• JSON, CSV, and Parquet support
• HTTP/2 support (which includes TLS, compression, random number generation, etc.)
• Support for asynchronous IO
• A logging abstraction
• A SQL client abstraction
• Key abstract data types (BTrees, hashmaps, sets, and growable arrays)
• Utilities for working with Unicode, time and timezones

But I don't think it needs to include:

• Excel support
• PostgreSQL or Oracle clients
• Flatbuffers support
• Niche data structures

Neither of these are intended to be complete lists, just examples.

Walled gardens

Minimal standard libraries force growing companies to build out their own internal collection of "standard libraries". As one example, Bloomberg did this with C++. And I've heard of companies doing this already with Rust. This allows larger companies to manage and minimize the explosion of transitive dependencies over time.

All growing companies likely do something like this eventually. But again, smaller standard libraries incentivize companies to build this internal standard library earlier on. And the community benefits relatively little from these internal standard libraries. The community would benefit more if large organizations contributed back to an actual standard library.

Smaller organizations do not have the capacity to build these internal standard libraries.

Maybe the situation will lead to libraries like Boost for JavaScript and Rust programmers. That could be fine.

Versioning

A comprehensive standard library does not prevent the language developers from releasing new versions of the standard library. It is trivial to do this with naming like Go has done with the v2 pattern. math/rand/v2 is an example.

Conclusion

I'm primarily thinking about maintainability, not security. You can read about the security risks of using a language with an ecosystem like Rust from someone who is an expert on the matter.

My concern about the standard library does not stop me from using Rust and JavaScript. They could choose to invest in the standard library at any time. We have already begun to see Bun and Deno to do exactly this. But it is clearly an area for improvement in Rust and JavaScript. And a mistake for other languages to avoid repeating.

While zero dependencies is practically impossible, everyone I've spoken to agrees that minimizing dependencies is ideal. Rust and JavaScript work against this ideal. But they could change at any time. And Bun and Deno are already examples of this.https://t.co/qkSh6oW1Yd pic.twitter.com/mY1MNErZG7

— Phil Eaton (@eatonphil) January 25, 2025