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i had graduated hs over the summer. no job, just home. it was 6/7am pdt, i just woke up and ran to the living room. could barely open my eyes. turned on the tv and every station was ny and i stopped at cnn and explosion and chills went down my spine. i didnt understand or could comprehend what was happening. i hadn't seen anything like it before. we saw oklahoma and others but this was planes. just chills. because back then there was no tsa and you could walk up all the way to the gate to see family off.

I was home in California. An employee called with the news. She was hysterical. Turned on the TV and watched the second plane. I went to work. It felt surreal, but on Thursday I called a vendor to get a status on a shipment coming from the east coast. They told me traffic was shut-down on the interstate highways. Nothing was moving.

I thought it might be interesting to give some concrete examples of how I use AI, because my use of it has moved quite a long way beyond simply opening ChatGPT and asking questions. Yes this has been written by AI but thoroughly checked and heavily prompted to get there. 😀 One of my main experiments has been building a self-hosted personal AI agent called Bud. Rather than being just another chatbot, Bud runs continuously on my own infrastructure and has gradually become a kind of personal AI operating layer. What Bud actually does Spiritual study Bud helps me with quite a lot of spiritual study and preparation. It can: prepare the day's text with key principles, meditation points and possible personal applications produce useful comments on every verse of scheduled Bible reading conduct deeper chapter studies using an indexed local library of publications search jw.org/WOL material that I have indexed locally use verified Bible wording rather than inventing or loosely paraphrasing scriptures I've built a fairly substantial local spiritual research library behind it. That now includes: more than 105,000 indexed passages from books, magazines, meeting workbooks and other material the complete NWT Study Edition — 1,189 chapters, including study notes, footnotes and cross-references around 2,260 Watchtower study articles 19 books cached from WOL So instead of relying entirely on general AI training data, the agent can search the actual source material before answering. That has been one of the most useful things I've learnt about AI: Give the AI good source material rather than expecting it to know everything. Health, fitness and rehabilitation I've also built a fully integrated fitness and rehabilitation application. It records workouts, exercises, progress and rehabilitation information, synchronises the information with my Obsidian vault, and then makes relevant information available to Bud. That means my AI agent can have an ongoing picture of my training and rehabilitation rather than each conversation beginning from scratch. It can help me: keep track of exercises and progress compare training sessions record rehabilitation information follow changes over time connect workout information with my wider notes I don't see this as replacing medical or professional advice. It's more like having a very organised record system that can also reason over the information already stored in it. Organisation and administration Bud also handles a lot of the everyday organisational work that is easy to forget. It can: maintain my calendar manage reminders and appointments keep a dedicated scheduling conversation as a single source of truth maintain durable notes record things I don't want to forget keep information in an Obsidian knowledge vault rather than losing everything when a chat ends This persistent information is something I find particularly useful. A normal chatbot conversation can be temporary. My aim has been to create something where useful information gradually becomes part of a structured personal knowledge system. AI helping with my secretary work Another very practical use I make of AI is writing and administration. In my congregation secretary role there are quite a lot of occasions where information needs to be communicated clearly, accurately and succinctly. AI is extremely useful for taking what I already want to say and helping me turn it into something clearer. For example, I use it to help with: organising rough notes making messages easier to understand shortening something without losing important information preparing concise administrative messages making wording warm and tactful removing repetition turning a collection of points into a logical message That is actually one of my most frequent uses of AI. Sometimes I know exactly what I want to communicate, but the AI can help me express it in two minutes rather than spending twenty minutes trying to get the wording right. For me, AI is often less about writing something for me and more about helping me communicate my own thoughts clearly and efficiently. Daily briefings Every morning at around 6:15am, Bud prepares a briefing covering things such as: what's on the calendar important reminders current priorities relevant health information things approaching that I need to think about It can also automatically produce: tomorrow previews weekly previews reminders scheduled spiritual material without me remembering to ask for them. That is where AI starts becoming quite different from a chatbot. Instead of: I remember something → open AI → ask AI it becomes: The system remembers → AI brings it to me when it matters. Research and monitoring Bud also watches various sources for me. For example, it can: monitor selected YouTube channels monitor AI developments summarise new material filter out things that aren't particularly important run research jobs delegate longer research to specialist agents This means AI can sometimes act less like a person I'm talking to and more like a piece of infrastructure quietly performing jobs I've previously asked it to do. My FPL manager One example is a complete Fantasy Premier League management system I've built. There are a number of FPL creators whose analysis I find useful. My system can collect and process their video transcripts and use that information alongside data about my own team. That gives me a way of bringing together many hours of FPL discussion and using it to help analyse things such as: transfers captaincy player form fixtures injuries upcoming deadlines different creators' opinions Rather than relying on one person's view, the system can help me see the overall picture across several different analysts. It's essentially a specialised AI research assistant built around one particular hobby. AI-assisted security camera monitoring Another project I've built is a Reolink camera watcher for security camera monitoring during LDC work. When you have perhaps 8–16 camera feeds visible at once, you still need to keep watching the screens properly, but anything that can draw attention to activity can be useful additional assistance. The program allows me to select the part of the computer screen containing the camera feeds and then monitors each of those individual feeds. It uses two layers of detection. First, it can watch for the person-detection icon produced by the Reolink cameras themselves. I've also integrated a YOLO computer-vision person detector, so the software can independently detect and track people appearing within the camera feeds. So it doesn't replace the person monitoring the cameras. It's an additional layer that can help draw attention towards one of many feeds when relevant activity is detected. That was another project that began simply with: "It would be useful if software could help with this." and eventually became something I could actually build. Multiple AI agents Another area I've experimented with is multi-agent systems. Bud doesn't necessarily have to perform every task itself. It can delegate work to other agents specialising in things such as: research coding debugging analysis I've experimented with tools and agents including Bud, Codex, Claude Code, OpenClaw, Antigravity and others. That means the workflow can sometimes look like: Me → Bud → specialist agent → result → Bud → me instead of me manually moving everything between different AI systems. About 80 specialised AI skills I've gradually built a library of roughly 80 specialist skills. A skill is basically a repeatable procedure teaching the AI how I want a certain type of job done. Examples include workflows for: talk preparation assistance Bible research family worship ideas rehabilitation tracking fantasy football YouTube monitoring project work coding system maintenance This is another change in how I now think about AI. Rather than repeatedly explaining: "Whenever I ask you to do this, please remember to do A, B, C, D and E…" I can encode that method once as a reusable skill. Around 47 scheduled jobs The system currently operates roughly 47 scheduled jobs. Some use AI. Others deliberately don't. They include things such as: daily briefings spiritual encouragement reminders monitoring backups housekeeping health checks weekly reviews data updates One useful lesson has been learning when NOT to use AI. If a normal script can reliably do something, I generally prefer the script. AI is most useful where judgement, interpretation, language or reasoning is required. So the architecture becomes something like: ordinary software for predictable work + AI for intelligent work. What I've actually learnt to build Perhaps the more interesting part for me isn't Bud itself. It's what experimenting with AI has enabled me to learn. I wouldn't describe myself as a professional programmer or AI engineer, yet with AI helping me I have been able to design and build systems I simply would not previously have attempted. AI system architecture I've learnt to put together systems involving: a main AI assistant specialist sub-agents task delegation scheduled jobs scripts databases knowledge stores local servers APIs monitoring dashboards backup systems recovery processes I can start with the outcome I want and use AI to help work backwards towards the architecture. Knowledge-base engineering One of the things I've enjoyed most has been creating specialist knowledge bases. Instead of merely hoping the model knows something, I can index authoritative material and tell it: Search this first. That is how my spiritual research system works. The AI can retrieve relevant material and then reason over it. That concept can be used with almost anything: personal notes technical documentation books research papers project histories specialist reference material The intelligence comes partly from the model, but also from giving the model the right information at the right time. Defining an AI's behaviour I've also learnt that building a useful AI agent isn't just about choosing the smartest model. A huge amount depends on defining things such as: its role its tone its boundaries what sources it must consult what it is allowed to change when it must ask permission how it should admit uncertainty For Bud I've maintained an identity and behaviour specification so that it stays warm and practical rather than becoming theatrical or pretending to know things it doesn't. I've also built rules such as: verify scripture wording search the local library for doctrinal research don't expose secrets restrict external systems where appropriate be clear about what the agent actually knows AI still needs boundaries. AI supervising AI I've increasingly become interested in using AI to supervise other AI. For example: AI architect → AI builder → AI tests → AI reviewer or: AI researcher → source checking → review → final synthesis That reduces my dependence on simply trusting the first answer produced by one model. Sometimes one of the most useful jobs for AI is checking the work produced by another AI. Automation with human approval I deliberately haven't tried to make everything fully autonomous. For important changes I prefer: AI proposes → I review → I approve → system implements That seems much more sensible than simply giving an AI unrestricted permission to modify everything. Automation is useful. Oversight is useful too. Keeping it economical I've also put quite a bit of thought into AI cost. Using the most powerful model for every task is unnecessary. I'm experimenting with systems that can choose between: simple deterministic code smaller/cheaper AI models general models coding models stronger reasoning models depending on what the job actually requires. The principle is roughly: Use the least expensive method capable of doing the job reliably. Only escalate to the more powerful models when their additional intelligence is actually needed. Other things I've built with AI Away from Bud, I've used AI to help design and build quite a variety of things. These include: the integrated fitness and rehabilitation system the Reolink/YOLO camera-monitoring assistant the complete FPL management system AI monitoring dashboards an agent observability system personal productivity tools project dashboards music-making software drum machines bass-line creators sub-bass generators ambient and psychedelic music tools workflow automation server monitoring databases research tools decision dashboards experimental multi-agent systems Often my part begins with something as vague as: "Wouldn't it be useful if something existed that could do this?" Then I can discuss it with AI until that becomes: idea → specification → architecture → design → code → testing → working application That, for me, is probably one of the most remarkable parts of current AI. The distance between having an idea and being capable of building the idea has suddenly become much smaller. Research The other major way I use AI is research. I've used it for subjects ranging from technology, AI and software through to health, history and Bible-related research. One particularly deep example has been my research into the Neo-Babylonian period. I've used AI to help investigate things such as: Babylonian kings dated business tablets regnal chronology astronomical records historical synchronisms ancient historical sources archaeological evidence Scriptural passages academic literature What makes AI useful here isn't simply asking it for an answer. I can give it a large research question and then keep drilling down into particular pieces of evidence. For example: "What is the original source for this claim?" "How strong is this evidence?" "Is this source contemporary with the event?" "Are these five websites actually relying on the same original source?" "Can we reconstruct the sequence year by year?" This is where AI can save an enormous amount of time while still leaving the human responsible for evaluating the evidence. Source-critical research I frequently ask AI not merely to find information, but to distinguish between different kinds of evidence. For example: primary evidence → contemporary records → near-contemporary records → later historical sources → modern academic interpretation → opinion That helps enormously with subjects where twenty websites may appear to provide twenty pieces of evidence when they're actually all repeating the same underlying claim. AI can help trace things back towards the original source. Researching current information For things that change frequently, I use AI together with live internet research. Examples include: developments in AI new AI models technology news software releases products prices travel and accommodation services technical documentation The big difference from an ordinary search engine is that I can search, compare the results, ask follow-up questions and have the material organised into something understandable. Researching videos and long material I also use AI to get useful information out of long videos and documents. I can give it a long video or transcript and ask: What were the main arguments? What evidence was presented? Which parts actually matter? What practical ideas came out of it? Is there anything here that I could use or build? Quite a few of my software projects have actually started because I've seen an idea somewhere and then asked AI: "Could we build our own version of that?" Writing and communicating clearly One of the less spectacular but probably most useful applications of AI for me is simply writing faster and more clearly. I use it constantly for: rewriting messages shortening long explanations improving structure removing repetition making something tactful making something warmer turning notes into finished prose organising research explaining complicated subjects simply producing summaries at different levels of detail I can often write a rough paragraph containing exactly what I mean and ask AI to preserve the meaning while improving the communication. That can save a surprising amount of time. And because I do quite a lot of administrative and congregation communication, that little everyday use of AI is probably just as valuable to me as some of the much more complicated systems I've built. Probably the biggest change I don't really think of AI as a chatbot anymore. Increasingly I think of it as a collection of capabilities: Researcher Programmer Architect Designer Critic Editor Scheduler Monitor Knowledge librarian Automation engine Orchestrator The clever part isn't necessarily any single AI model. It's increasingly about combining: AI + ordinary software + trusted source material + automation + databases + human oversight. And ultimately that is what I find most useful about it. I'm not trying to automate my life for the sake of automating it. I'm trying to remove some of the repetitive mental load, speed up things that otherwise take unnecessary time, and make information easier to manage so that more attention can go towards the things that actually matter. And occasionally, of course, using a ridiculously powerful collection of AI models to build something completely unnecessary simply because I thought, "That would be brilliant." 😄

I was at work. Radios and TV'S were not allowed during working hours but the supervisor let everyone watch the news that day.

Give hints !

Back from high school... I saw the second plane entering

Ah, you’re a teaser, aren’t you! 😆

I just back from a German class and my dad told me: „You won’t believe it. Some airplanes crashed into skyscrapers in New York.” I remember I couldn’t understand him. It didn’t make any sense.

second grade

French Class. They interrupted to make a somber announcement that I didn't really understand the import of. My family and I were glued to the TV for the rest of the day.

What I can say for now is that LY, commonly known as WTLIB, is getting some major improvements behind the scenes. These changes will also affect jw.org. It’s been a busy few weeks for me ahead of AM. Hehe. I’ll let y’all spot the changes, not just in LY but across the website too. Some are already starting to show, so you just have to look for them.

September 11, 2001 🏙️💐 What a horrible day! I’ll never forget it. I was at work, and after that day, it felt like a whole series of events had been set in motion. Where were you guys that day?

The installation package is just the application, without any data. The data is being downloaded separately. As of now, the latest library version for English is 2604. Not even the November 2026 study Watchtower is in this package.

No significant changes. Build number increased to 14346.

Is there new data? Does it have the December 2026 Study WT?

If you can, please show screenshots of what it looks like after full installation. Unless it doesn't look significantly than previous version. Thanks

I remember "Colossus: The Forbin Project" it was pretty scary at the time.

@Shawnster apologies my friend. yes. 🙏

New Watchtower Library Update Requires reinstallation

Hard to believe it's been 25 years.

Liquid Glass and the unique iOS design for the iPhone Duo probably mean even more work for our JW Library® developers at WHQ. With a new JW Library update coming soon, We really appreciate all their hard work to keep our apps up to date, running smoothly, and secure.

I think a logical developement could be that Jehovah puts it into the kings' hearts to surrender their power to the UN. And after having conquered a rouge AI, the UN looks at religion ... Hmmm ... It could be closer than we think ❤️ We live in exciting times!

It’s interesting to see that when God made people, he gave them potential limitless intelligence and free agency. They squandered it, but it’ll never get to the point where Jehovah says: “Oh no! I’ve lost control, they’ve gone too far, they’ll permanently destroy everything.” Human though are feeding their inventions with almost the same potential and yet they fear eventual total lack of control over it. How superior God’s ways are!

Indeed! I heard about this via the news and a guest on TV show recently! Thanks for posting

I thought this might make an interesting topic for discussion. Over the last few days there has been quite a lot of coverage in respected mainstream news sources about concerns being raised by researchers actually working at some of the leading AI companies. What particularly caught my attention was a comment from Evan Hubinger, an alignment researcher at Anthropic, who said: “We really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade.” Obviously that doesn't mean scientists have calculated that there is literally a 10% probability that AI will wipe out mankind. This is Hubinger's personal assessment of the risk, and probabilities involving something unprecedented like superintelligent AI are clearly extremely difficult to quantify. But I do find it remarkable that people working near the forefront of this technology are openly talking in these terms — and that Reuters, the Financial Times, Washington Post, Wall Street Journal, Guardian and CNN are now treating the subject seriously. Some of the recent coverage: Reuters — “More US lawmakers seek new AI rules after Anthropic researchers warn of human extinction” https://www.reuters.com/business/openai-faces-senate-probe-into-hugging-face-incident-axios-reports-2026-09-10/ Reuters reports that the warnings from Anthropic researchers have now prompted calls from US lawmakers for stronger AI safety regulation. The Guardian — “Anthropic researchers say AI could cause human extinction by 2030” https://www.theguardian.com/technology/2026/sep/09/anthropic-researchers-ai-human-extinction This covers the comments from Jacob Coxon, Evan Hubinger and Samuel Marks. Hubinger gives his personal estimate as greater than 10% within the next decade. Washington Post — “Political world erupts as AI researchers warn of ‘extinction’ threat” https://www.washingtonpost.com/technology/2026/09/09/anthropic-researcher-resigns-warning-reckless-race-toward-superintelligence/ The Washington Post reports Hubinger's >10% estimate and discusses the fact that Anthropic has deliberately built a reputation for being one of the more safety-conscious AI companies. Financial Times — “Anthropic researcher quits over AI labs ‘gambling with our lives’” https://www.ft.com/content/20c07191-8da6-440f-b04b-8ea0ebdd9153 The FT reports on researcher Jacob Coxon's resignation from Anthropic and his concern that companies are racing towards increasingly powerful, potentially self-improving AI without having solved the problem of reliably controlling it. Wall Street Journal — “How Would AI Actually Kill Us All? What to Know About the AI Doomsday Debate” https://www.wsj.com/tech/ai/how-would-ai-actually-kill-us-all-what-to-know-about-the-ai-doomsday-debate-034270d1 The WSJ looks at what researchers actually mean when they talk about an existential AI risk — things such as losing control of very capable autonomous systems or AI dramatically increasing people's ability to create biological or other catastrophic weapons. CNN — discussion of the Anthropic warnings https://transcripts.cnn.com/show/cg/date/2026-09-09/segment/01 CNN also reports Hubinger's greater-than-10% estimate, while importantly pointing out that any precise percentage should be regarded as a judgment rather than a measured probability. Of course, from a Scriptural point of view, we don't believe that AI — or anything else humans develop — is ultimately going to frustrate Jehovah's purpose for mankind and the earth. Jehovah says that he formed the earth “to be inhabited” (Isaiah 45:18), and Psalm 37:29 says that “the righteous will possess the earth, and they will live forever on it.” So I'm certainly not suggesting that AI is actually going to exterminate humanity. What interests me is the contrast: mankind is developing technology so powerful that some of the people closest to it are now publicly discussing the possibility of losing control of it, to the point that human extinction is appearing in mainstream political and scientific discussion. Only a few years ago that would probably have sounded like science fiction. What do you make of these warnings? Do you think researchers at the forefront of AI are seeing a genuine danger that most people haven't appreciated yet? Or is putting figures such as “10%” on something this uncertain likely to exaggerate a risk that nobody can really calculate?