I’ve been building quantum photonics experiments. Repeating the Bell inequality tests that won the 2022 Nobel, quantum erasers, etc.
I just published a fun interactive 3D demo of SPDC, one of the most common and accessible ways to create entangled pairs of photons. I'm hoping to publish a series of articles on other cool learnings about doing quantum photonics in the lab.
I’ve been building quantum photonics experiments. Repeating the Bell inequality tests that won the 2022 Nobel, quantum erasers, etc.
Probably the coolest part has been automating the optomechanical equipment and optimizing physical experiments with Bayesian optimization. Similar to hyperparameter tuning in ML, but with lasers.
Recorded 10 February 2026. Terence Tao of the University of California, Los Angeles, presents "Machine assistance and the future of research mathematics" at IPAM's AI for Science Kickoff.
I appreciate the ability to rapidly capture a note/thought/todo without friction and context switching.
I solved this problem with a twilio sms number. When I send a text to it, the content gets prepended to my obsidian todo.md file. This was easy to arrange with a few lines of Python glue.
iOS makes it easy to text or share to sms from almost any context.
The Earth is generally expected to spin more slowly over time, due to tidal friction. But it has been spinning faster and faster since the 1960s. As shown in the figure in the wikipedia article [0].
I have read numerous explanations, but haven't found a really authoritative discussion.
Wow that's really interesting. A great quote form the article:
> In 2021, it was reported that Earth was spinning faster in 2020 and experienced the 28 shortest days since 1960, each of which lasted less than 86399.999 seconds.[24] This caused engineers worldwide to discuss a negative leap second and other possible timekeeping measures, some of which could eliminate leap seconds.[25] The shortest day ever recorded was 29 June 2022, at 1.59 milliseconds less than 24 hours.[26] In a 2024 paper published in Nature, Duncan Agnew of the Scripps Institution of Oceanography projects that the water from increasing ice cap melting will migrate to the equator and thus cause the rate of rotation to slow down again.[26]
Aider actually prompts the model to say if it needs to see additional files. Whenever the model mentions file names, aider asks the user if they should be added to context.
As well, any files or symbols mentioned by the model are noted. They influence the repomap ranking algorithm, so subsequent requests have even more relevant repository context.
This is designed as a sort of implicit search and ranking flow. The blog article doesn’t get into any of this detail, but much of this has been around and working well since 2023.
I see, so the context adapts as the LLM interacts with the codebase across requests?
That's a clever implicit flow for ranking.
The difference in my approach is that exploration is happening within a single task, autonomously. The agent traces through structure, symbols, implementations, callers in many sequential lookups without human interaction. New files are automatically picked up with filesystem watching, but the core value is that the LLM can navigate the code base the same way that I might.
Are you using LLM to help you write these replies, or are you just picking up their stylistic phrasings the way expressions go viral at an office till everyone is saying them?
As an LLM, you wouldn't consider that you're replying confidently and dismissively while clearly having no personal experience with the CLI coding agent that not only started it all but for a year (eternity in this space) was so far ahead of upstarts (especially the VSCode forks family) it was like a secret weapon. And still is in many ways thanks to its long lead and being the carefully curated labor of a thoughtful mind.
As a dev seeking to improve on SOTA, having no awareness of the progenitor and the techniques one most do better than, seems like a blind spot worth digging into before dismissing. Aider's benchmarks on practical applicability of model advancements vs. regressions in code editing observably drove both OpenAI and Anthropic to pay closer attention and improve SOTA for everyone.
Aider was onto something, and you are onto something, pushing forward the 'semantic' understanding. It's worth absorbing everything Paul documented and blogged, and spending some time in Aider to enrich a feel of what Claude Code chose to do the same or differently, which ideas may be better, and what could be done next to go further.
I’m building a quantum photonics experiment that is a variation of the quantum eraser.
One aspect that HN may find interesting is my use of Bayesian optimization to control and perfect key experimental settings. About a dozen of the wave plates and other optical components are motorized and under computer control.
Given a goal metric like "maximally entangle the photon pairs" the optimizer will run the experiment 50-100 times, tweaking the angles of various optics and collecting data. Ultimately it will learn to maximize the given cost function.
This sort of thing is commonly done with tools like Optuna during NN/LLM training to optimize hyper-parameters, but seems less common in physics especially quantum photonics. I'm using a great tool called M-loop to drive the optimization, which was originally developed for creating Bose-Einstein condensates.
I just published a fun interactive 3D demo of SPDC, one of the most common and accessible ways to create entangled pairs of photons. I'm hoping to publish a series of articles on other cool learnings about doing quantum photonics in the lab.
https://paulg.info/2026/04/10/spdc/
reply