S Anand

At work, we have to change passwords every 2 months. The password rules are stringent and it typically takes me 10 minutes to find one that works. Here are the rules: The password being setup must have a minimum length of 12 Minimum number of special character to be included in the new password is 1 Must contain at least 1 upper case character(s) Must contain at least 1 lower case character(s) Minimum number of numeric characters to be included in the new password is 1 The construction of the password cannot contain Palindromes. The system has been configured to prevent such passwords The system is configured to not allow the last 24 passwords. A password once setup is valid for 60 days after which it expires. A password once setup must be used for a minimum of 2 days A list of common words has been restricted to be used as passwords. This list is available on the password change page of the self service portal The construction of the password must not contain 5 consecutive characters from the Username or previously used passwords This is so complex. Rules #5 and #8 makes it especially tough. I tried all my standard password strategies: ...

Last week, I saw that local agents are good but slow. Today, I benchmarked the speed and cost. On my NVIDIA RTX 2000 GPU, I can run gemma4:e4b-it-qat at ~60 tokens per second. That seems the best intelligence performance I can get right now. It has an Artificial Analysis intelligence index of ~9 without reasoning and 12 with reasoning. So, if I run it for an hour, it’ll save me the equivalent cost of about 8-12 cents in API calls. ...

This week, I learned: I know that fact-checking 2000 page PDFs is error-prone, so’d do it manually for a few pages, then refine. Agents would know this if they’ve tried and failed and added it to their memory systems. So I intervene when agents don’t remember well (increasingly rare) or I think they haven’t seen it before (again, increasingly rare). My prompts guard against these - but I shouldn’t habitualize these - they’ll be needed less in future. rofi - a Linux menu app I use for all kinds of things - makes it hard to take screenshots because it takes over focus and I can’t send the PrtSc or other keys to the screenshot apps. So I use flameshot full --delay 3000 --path ~/Downloads/screenshot.png to take a screenshot (of the last region it used) 3 seconds later, and quickly activate rofi in-between. (The docs say flameshot full captures the full screen. For me, it captured my last region.) ChatGPT can now connect to multiple GMail accounts in paid plans. X AWS to acquire DuckDB. DuckDB is one of my favorite tools today and is improving at a rapid pace. I guess that will continue in the short run - hope it lasts long enough for a worthy successor. I use GoatCounter to track my website traffic. It doesn’t capture domains (only paths), so I just use a code snippet to log https://sanand0.github.io/SITE/... as /@SITE/... - the @ makes it easy for me to see that it’s from a GitHub page. I switched from anydoc to docling as my default PDF to Markdown converter. Docling is even better than AnyDoc #ForNow. Based on my Codex usage, ChatGPT recommended that I not create custom subagents (my setup already used subagents a fair bit last month), but rather, just use default_subagent_model = "gpt-5.6-luna" (which is a remarkably good model for its price #ForNow). Artificial Analysis hosts several useful task-based evaluations. Here are some the fronter leaders #ForNow: GDPVal is good for realistic tasks. Again, GPT 5.6 Luna models have captured most of the low-cost fronter. EnterpriseOps Gym is good for office tasks like emails, calendars, Teams/Drive, support tasks, etc. Interestingly, none of the OpenAI models are on the frontier. Claude Fable 5 and Gemini 3.5 Flash are at the high-end, but Kimi K3 (Max), Qwen3.7 (Max), GLM-5.2 (Max) lead the mid-end and DeepSeek V4 Pro / Flash lead the low-end frontier. TerminalBench 2.1 is good for coding tasks. The OpenAI models completely rule this frontier. Analyst Agent is good for spreadsheet tasks (using Python). Gemini 3.7 Flash seems to be a strong outlier on this frontier. Tau3 is good for navigating unstructured data. GPT 5.6 Luna wins the bottom, DeepSeek V4 the middle, and Qwen 3.8 / GLM 5.3 take the top. Anthropic and Google are not even in the race. Briefcase is good for building useful things from diverse messy data. Grok completely wins the mid-end with Anthropic dominating the high-cost frontier. APEX Agents is good for deeper knowledge work. GPT 5.6 Luna (Max) is a strong frontier outlier here. Task-based benchmarks seem more practical than token-based benchmarks. For example, rather than the LM Arena ELO vs Cost, I would use Terminal Bench 3.0 which clearly tells me to use Codex with GPT-5.6 Sol over Claude Code with Opus 5 / Fable 5; that GLM 5.3 with Claude Code might be excellent value for money as well when using APIs. #ForNow I also did a rough calculation to see if the $18 GLM subscription offers more than $20 ChatGPT Plus. Short answer: No, for my usage, Codex offers ~1.8x more value #ForNow. ChatGPT Anthropic’s Skill Creator Skill now includes evals and A/B testing #ForNow to check if a skill actually does better. I’ve been doing this the hard way, e.g. with impact of simple writing on thinking, optimizing my ideation prompt, optimizing my summarization prompt, etc. I switched from markitdown to anydoc as my default PDF to Markdown converter. AnyDoc handles tables and other kinds of structures in PDF much better #ForNow. Questions I was asked Week ending 30 Aug 2026 ...

I save prompts and prompt fragments I regularly use with ChatGPT, Claude, etc. (Prompt fragments are just prompts used along with other prompts. They’re typically smaller. But the difference isn’t important or anything… I just use two methods.) I use a script triggered by Ctrl Alt P to select the prompt to paste. This month, the five prompts / fragments I used the most were: #5: Reframe question skill. Sometimes, I’m not sure I’m asking the right question. Actually, I’m not even sure what I’m asking. ...

This week, I learned: DuckDB 2.0 adds a CONNECT command that can connect to databases like MySQL, PostgreSQL, etc. making DuckDB the only DB client I need. EQ-Bench evaluates models on capabilities like: does it follow direction, does it challenge you, how good are its insights, does it build rapport, etc. Very interesting to see that the Gemini models are the most “yielding” to your pressure and “validating” your beliefs (Anthropic’s are the least) while OpenAI models are the most “directive” (give concrete actions) #ForNow. There are other benchmarks such as Creative writing which Opus 5, Kimi K3, and GPT-5.6 Sol lead #ForNow. OpenRouter offers several models at a discount. #ForNow, GPT-5.6 Sol is at a 50% discount, DeepSeek v4 Pro at 62%, and Gemini 3.7 Flash at 75% discount. There’s also a Free Models collection that #ForNow includes Nemotron 3 Ultra and more. For a few years, I’ve been feeling useless, that I don’t contribute anything tangible to my organization. No measurable metric I’ve improved. Today, it strikes me that this is a good thing if I don’t want to be fired. As AI eats up more of our work, measurable contributions naturally shrink (AI does more, you do less/different work), and the vague “Oh, he’s probably doing some good” is a safer bet than “He contributed 10% to this metric last year, this year it’s 1%, can we justify his cost?” (I’m sure marketers will come up with a good term to cover this feeling of uselessness that is actually a good thing.) ChatGPT Desktop - Work is a layer on top of Codex #ForNow (which I sort-of expected, but the session logs confirm this). It adds instructions that cover: Memory: from memory_summary.md, MEMORY.md, rollout summaries, and saved skill notes. Recheck decaying ones, mention if unverified. Folders: Temo work in work/, final in outputs/, local files use absolute paths. Coordination: How to start, fork, inspect, message, wait for, rename, … Codex tasks, how to use subagents. Automations: Available tools for reminders, schedules, monitors, follow-ups, and wake-ups. Knowledge management: known project → memory; specialist task → skill; external object → connector; subtask → subagent; recurring work → automation; finished artifact → Work UI primitive. Presentation: Use shell/scripts internally but hide it, describe outcomes in user terms. Apps/Connectors: Gmail, Drive, GitHub, Dropbox, etc. Skills: via SKILL.md Neither ChatGPT Work nor Claude Work can read the ChatGPT / Claude chat conversations. But the chat conversations can access past conversations via “Memory”. That’s a pity, and one of the reasons I’m more often on “chat” than on “work” - it can refer to my past chats automatically, which helps build a kind of unstructured knowledge base. The other reason is that, at least on ChatGPT, chat does not consume usage limits #ForNow. ChatGPT work and Claude - both chat and work - consume usage limits. Weird that there’s a “make a lot of money” button and nobody’s pressing it (take your SaaS, make it headless, let agents use it, charge per interaction esp for enterprises). Thariq AI is accelerating discoveries in cyber (definitely) and maths (reasonably) but not as much in algorithms. METR “Match your prompt style to the desired output.” Clear guidance from Anthropic that “The formatting style used in your prompt may influence Claude’s response style.” OpenAI says something similar - adapting implicitly to the user’s tone. But this is not a very strong signal - examples are better guides. Why model routing must be in the harness. Makes perfect sense. “Only the harness can judge when a model switch is worth the cache miss.” I’m sure some popular harness (like OpenCode, Codex, Claude Code) will enable an “auto model” mode that’ll pick and change the model by itself by the end of the year. Microsoft Print to PDF can, sometimes, generate PDFs with no highlightable or selectable text - all fonts get converted to paths. A crude solution is below. This is a poor solution but often good enough for an LLM to process. (Of course, if you’re passing it to an agent, you could just upload the file and it’ll figure it out.) sudo apt install ocrmypdf tesseract-ocr ocrmypdf --output-type pdf input.pdf ocr.pdf pdftotext ocr.pdf - When my train neighbor started talking to me (asking personal questions but was self-aware, rambling but was partly interesting), I asked if he was an extrovert. He said “No”. People who talk a lot can still be introverts if they’re: socially competent (like me at work) in “performance mode” (like me when I’m on stage) are high energy and engaged by topics (maybe him - or me when, like now, when I just HAVE to tell the flight attendant Ollama + Gemma 4 + Pi answering a psychology question is a delight!) ambiverts (maybe him) not self-aware and are mistaken (maybe him) ffmpeg can embed subtitles. ffmpeg -i video.webm -i subtitles.srt -map 0:v -map 0:a? -map 1:0 -c:v copy -c:a copy -c:s srt -metadata:s:s:0 language=eng -metadata:s:s:0 title="English" -disposition:s:0 default output.mkv adds subtitles.srt to video.webm and creates output.mkv with embedded subtitles. Note: On VLC, MKV works better than WEBM if you want to embed subtitles. On the browser, you need to use the <video> tag with a <track> tag to display subtitles. ffmpeg can burn subtitles. ffmpeg -i video.webm -vf "subtitles=subtitles.srt" -c:v libvpx-vp9 -crf 30 -b:v 0 -c:a copy output.webm re-encodes the video with subtitles added to the video. ffmpeg can offset subtitles. For example: ffmpeg -itsoffset 10 -i input.srt -c copy output.srt creates output.srt with subtitles starting 10 seconds later than input.srt. When I hear my father’s tales from his childhood, I’m struck by how much India moved forward in half a century on child mortality, consumerism, and communication (mobiles). Also surprising are what feels the same: legal system, travel (trains made it easy), food (tasted better, then), entertainment (theatres made it easy), gardening, education (scholarships made international study more accessible than I thought), I asked ChatGPT how I adapt my message based on the audience. It discovered that I tailor messages to the audience’s (A) Objectives (B) Examples (C) Expertise - e.g. tell vs ask (D) Risk appetite. But what’s distinctive is that I often surrender, i.e. I don’t defend my view, but drop it and run with their framing. “People with taste are picky. The only way to make money is by satisfying those who can’t discern quality.” Adrian Hanft. I’ve been telling people that taste is our differentiator against AI. And yes, it’s a fickle differentiator. I mean, how do you build a taste that thousands or millions will adopt? Or… is taste marketed more often than organically adopted, in which case, persuasiveness matters more than taste? But either way, unless most people disagree with you, you’re building conformity, not taste. On a flight, I tried ollama launch pi --model gemma4:e4b-it-qat. It’s a reasonably sensible model. Power consumption is high, though. I was at about 8 watts with ~7 hours of battery life. While running, power spiked to ~50W (1.5h) and settled down to ~12W (4h) when idle. The llama-server process consumes some CPU/GPU even when idle, but I couldn’t get it back to the ~8W even after ollama stop. (It eventually did return to 8W after an hour. Not sure why.) When I tried again on 21 Aug, it went up from 8W (8h life) to 28W (3h life) and back to 8W, so looks like when idle, it doesn’t consume power. I look forward to using local LLMs more! Pain is good. Struggle is good. Stretch is good. Not new. But worth reminding, worth seeking. T3 Code is a coding agent orchestrator. It lets you “remote control” multiple coding agent sessions across systems. The ecosystem of tools around coding agents is growing. Observability, e.g. AgentsView, is one such area. Questions I was asked Week ending 23 Aug 2026 ...

Some ideas on what we can do less, thanks to AI. Why? Because less is more and we keep getting advice on what to do more of, not what to do less of. Skip meetings you don’t speak in. Agents can summarize the transcript. Stop taking notes. Agents can summarize a recording. Stop reading group chats. Agents can summarize WhatsApp groups, Hacker News, Twitter, and more. Stop translations. Agents translate on the fly. For you, and your audience. Stop weekly status reports. Agents can tell you (and your manager and team) the status. Stop building dashboards. Agents tell you “what’s wrong” or “what’s next” directly. Stop building software at first. Tell the agent to do the work. Let it write software if needed. Save it if you use it often. Stop writing requirements. Agents can prototype. Test if that’s what you need, first. Stop teaching coding. Agents can code. Teach people to use agents. Also - a few things that were true pre-AI: ...

Last year, I shared how local LLMs are expensive. There are only 3 reasons to use them over an inference API: You have no choice, e.g. Government, pharma, finance, or other sensitive data. You have free GPUs, e.g. your laptop, local data center with sunk cost, free cloud credits, etc. This doesn’t scale. You want to learn. This year, I finally started using them for a combination of #1 (on flights) and #2 (my 8 GB NVIDIA RTX 2000). ...

Over the last few weeks, I asked 166 people: What’s something you did not ask AI last week, because it can’t do it? There were 4 groups of responses. 37% said “Reliability”. AI tries but fails often enough. Like: Verify & return exact source of information. Generate accurate images. Completing PDF accessibility reliably according to the WCAG 2.2 standard. Converting 2D interior design to 3D view as vision models are not good keeping things as per scale. Create a user manual based on my codebase. I have asked this before and it has failed. 27% spoke about “Reach”. AI can’t access tools, data, or do something. ...

There are some small pleasures in life, not worth mentioning by themselves, but sometimes they come together so well. Like: Google Pay popping up a “Disable verification for transit?” option, so I just touch my phone to the bus ticket reader even while on a call and it just works like my card. (It works even without Internet!) Finding a seat at the front of the double-decker bus on top, so I can take in the entire vista from a height for the entire one hour ride Discovering the ultra-configurable Open Camera app (thanks to Thej) and capturing at the much smaller 640x480 resolution at 10x speed Transferring it like a breeze to my laptop with LocalSend Compressing it into an ultra-small 1.4MB video thanks to the AV1 codec and FFmpeg Publishing it to Cloudflare within a second thanks to ultra-fast WiFi Watch the video Yeah… this is life!

Two months ago, I was impressed at the ingenuity of a lady who used a plastic cover to watch videos on a flight. Today, on the same Indigo flight from Singapore to Chennai, I saw a man one seat ahead of me use the headrest cover on the seat in front to watch videos. (The headrest cover is the cloth behind your head on airline seats. It saves cleaning effort.) ...