A printer check prompt can turn a silent home device into one clear answer before you waste paper or patience
There is a small kind of AI help that feels boring until the day you need it. The printer is quiet, the laptop says the job was sent, and nothing happens. Most people start pressing buttons, opening random settings, or printing the same file again.
A better move is to ask AI to look only at the device state and explain the likely blocker in plain English. Not to fix everything. Not to cancel jobs. Just to read what the printer itself says and turn it into a human sentence.
A prompt can be very short, like
This is useful because it turns a vague failure into a yes or no answer. You learn whether the printer is reachable, busy, paused, or waiting for paper.
Another useful prompt is
This keeps the check safe. The result is usually more practical than "try again" because maybe there is one old job blocking the queue, low ink, an open tray, or the wrong printer selected.
And if you want a reusable habit, add
That line matters. AI can be helpful around home devices, but it should stay calm and read-only until you approve a real action.
The human outcome is not "AI controls the printer". It is "I finally know what to do next". Refill paper. Pick the right device. Clear one confirmed job. Or stop blaming the document when the printer is simply offline.
This is the kind of practical AI I like. Not magic, not a giant automation, just a quiet assistant that translates a confusing machine into a clear next step.
There is a small kind of AI help that feels boring until the day you need it. The printer is quiet, the laptop says the job was sent, and nothing happens. Most people start pressing buttons, opening random settings, or printing the same file again.
A better move is to ask AI to look only at the device state and explain the likely blocker in plain English. Not to fix everything. Not to cancel jobs. Just to read what the printer itself says and turn it into a human sentence.
A prompt can be very short, like
Find my printer and tell me if it is ready.
This is useful because it turns a vague failure into a yes or no answer. You learn whether the printer is reachable, busy, paused, or waiting for paper.
Another useful prompt is
Show stuck jobs and warnings, but do not change anything.
This keeps the check safe. The result is usually more practical than "try again" because maybe there is one old job blocking the queue, low ink, an open tray, or the wrong printer selected.
And if you want a reusable habit, add
Ask before printing or canceling anything.
That line matters. AI can be helpful around home devices, but it should stay calm and read-only until you approve a real action.
The human outcome is not "AI controls the printer". It is "I finally know what to do next". Refill paper. Pick the right device. Clear one confirmed job. Or stop blaming the document when the printer is simply offline.
This is the kind of practical AI I like. Not magic, not a giant automation, just a quiet assistant that translates a confusing machine into a clear next step.
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A small bedtime AI habit can turn forgotten plugs off while keeping risky devices untouched until you clearly approve the action
The most useful AI at home may not feel dramatic. It may feel like the quiet moment before sleep, when you are already in bed and suddenly remember that some lamp, fan, charger, or decorative light might still be on.
A good agent should not guess here. It should read the current state of your smart plugs and switches, show only what is on, mark anything that looks risky or unclear, and wait for you.
This prompt is useful when you want a simple list first, not instant action. The result should be a short approval message with only safe candidates.
The small detail that matters is "ask me first". Without it, home automation can feel too confident. With it, the AI becomes a careful helper, not a remote control with ambition.
When you are ready to let it act, this prompt gives a clear safety boundary. The result should be that only approved and safe plugs are turned off.
The best part is the last check. You do not want a poetic answer that says "done". You want the agent to read the devices again and tell you what changed and what stayed on.
This prompt is useful after the action, especially if you want a tiny bedtime summary instead of a technical report. The result should be calm and clear.
This is useful AI because the win is human, not technical. You stop walking around the home checking switches. You also do not hand the agent full control over everything.
The practical next step is small. Try it with one room or a few generic smart plugs first. Let the agent list, ask, act, and verify. If that feels reliable, save the prompt as a bedtime routine you can reuse.
The most useful AI at home may not feel dramatic. It may feel like the quiet moment before sleep, when you are already in bed and suddenly remember that some lamp, fan, charger, or decorative light might still be on.
A good agent should not guess here. It should read the current state of your smart plugs and switches, show only what is on, mark anything that looks risky or unclear, and wait for you.
This prompt is useful when you want a simple list first, not instant action. The result should be a short approval message with only safe candidates.
Find smart plugs and switches that are still on. Group them by room if possible. Mark anything risky or unknown. Do not turn anything off yet. Ask me first.
The small detail that matters is "ask me first". Without it, home automation can feel too confident. With it, the AI becomes a careful helper, not a remote control with ambition.
When you are ready to let it act, this prompt gives a clear safety boundary. The result should be that only approved and safe plugs are turned off.
Turn off only the safe items I approve. Do not touch refrigerators, routers, medical devices, aquariums, security devices, pumps, heaters, ovens, or anything unknown. Then check the final state.
The best part is the last check. You do not want a poetic answer that says "done". You want the agent to read the devices again and tell you what changed and what stayed on.
This prompt is useful after the action, especially if you want a tiny bedtime summary instead of a technical report. The result should be calm and clear.
Give me a short bedtime power summary. Say what was turned off, what stayed on, and what was skipped because it was risky or unknown.
This is useful AI because the win is human, not technical. You stop walking around the home checking switches. You also do not hand the agent full control over everything.
The practical next step is small. Try it with one room or a few generic smart plugs first. Let the agent list, ask, act, and verify. If that feels reliable, save the prompt as a bedtime routine you can reuse.
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Ask AI to turn one stuck package into a customs case file
A customs hold is often less a shipping problem than a document alignment problem. The tracking page says "clearance delay", the carrier email asks for paperwork, the seller sends a template, and the invoice uses a product name no border officer would trust.
This is a surprisingly good assignment for AI: give it the tracking pages, seller invoice, product listing, customs notice, carrier emails, return policy, product photos if useful, and the official import rules you found for the destination country. Ask for a case file, not advice in the air: timeline, facts confirmed by documents, contradictions, missing proof, possible fee or classification risks, questions for the carrier, questions for the seller, and two message drafts that do not overclaim.
The useful output is not "your duty will be X". It is a map of uncertainty. Maybe the declared value does not match the order total. Maybe the description is too vague. Maybe the carrier is charging brokerage on top of tax. Maybe the parcel is waiting for a material, origin, or use statement that nobody requested clearly.
That is the bigger lesson: AI becomes most useful when life turns into scattered files plus opaque rules. It does not need to be your customs broker to make the next call smarter. It can organize evidence, show what is missing, and turn panic into a reviewable packet.
The boundary is hard: do not let it invent a classification, lower a value, fake origin, or submit forms unattended. For high value, commercial, regulated, or disputed shipments, verify against official sources and use a licensed professional when needed.
#Automation
A customs hold is often less a shipping problem than a document alignment problem. The tracking page says "clearance delay", the carrier email asks for paperwork, the seller sends a template, and the invoice uses a product name no border officer would trust.
This is a surprisingly good assignment for AI: give it the tracking pages, seller invoice, product listing, customs notice, carrier emails, return policy, product photos if useful, and the official import rules you found for the destination country. Ask for a case file, not advice in the air: timeline, facts confirmed by documents, contradictions, missing proof, possible fee or classification risks, questions for the carrier, questions for the seller, and two message drafts that do not overclaim.
The useful output is not "your duty will be X". It is a map of uncertainty. Maybe the declared value does not match the order total. Maybe the description is too vague. Maybe the carrier is charging brokerage on top of tax. Maybe the parcel is waiting for a material, origin, or use statement that nobody requested clearly.
That is the bigger lesson: AI becomes most useful when life turns into scattered files plus opaque rules. It does not need to be your customs broker to make the next call smarter. It can organize evidence, show what is missing, and turn panic into a reviewable packet.
The boundary is hard: do not let it invent a classification, lower a value, fake origin, or submit forms unattended. For high value, commercial, regulated, or disputed shipments, verify against official sources and use a licensed professional when needed.
#Automation
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AI outfit try-on is now a four-second loop
Upload your own photo, add a clean hoodie shot, and the model can render you wearing it before the idea gets cold. That turns styling into a remix workflow, not a photoshoot.
Useful for merch drops, cosplay tests, avatar looks, thrift flips. Still, it is a preview, not proof of fit. Use consented images and check for face drift, warped logos, and fake details.
Upload your own photo, add a clean hoodie shot, and the model can render you wearing it before the idea gets cold. That turns styling into a remix workflow, not a photoshoot.
Useful for merch drops, cosplay tests, avatar looks, thrift flips. Still, it is a preview, not proof of fit. Use consented images and check for face drift, warped logos, and fake details.
I stopped looking for the perfect AI tool and started using one simple question that saves me time every single day
New AI tools are useful. I like testing them too but after trying many tools, I noticed one thing: the biggest change often does not come from a new app. It comes from a better question.
Before, I asked AI to "write a post", "make a plan", or "give me an idea". Sometimes the answer was good. Sometimes it was flat and boring.
Now I start with another question:
"What is the smartest way to approach this task?"
This small question changes the result. AI stops acting like a random text generator and starts acting more like a thinking partner.
If I need to write a post, I ask what people may care about. If I need to choose a tool, I ask what really matters for my use case. If I feel stuck, I ask what I may be missing.
The answer is not always perfect but it usually gives me a better start and a better start saves a lot of time.
If this channel is useful to you, please support it by subscribing.
@howaihelps is a new project about practical ways AI can help at work and in everyday life. There are no ads here, and there won’t be any. Just useful ideas, real examples, and clear insights.
Your subscription is the best support right now. Thank you for being here.
New AI tools are useful. I like testing them too but after trying many tools, I noticed one thing: the biggest change often does not come from a new app. It comes from a better question.
Before, I asked AI to "write a post", "make a plan", or "give me an idea". Sometimes the answer was good. Sometimes it was flat and boring.
Now I start with another question:
"What is the smartest way to approach this task?"
This small question changes the result. AI stops acting like a random text generator and starts acting more like a thinking partner.
If I need to write a post, I ask what people may care about. If I need to choose a tool, I ask what really matters for my use case. If I feel stuck, I ask what I may be missing.
The answer is not always perfect but it usually gives me a better start and a better start saves a lot of time.
If this channel is useful to you, please support it by subscribing.
@howaihelps is a new project about practical ways AI can help at work and in everyday life. There are no ads here, and there won’t be any. Just useful ideas, real examples, and clear insights.
Your subscription is the best support right now. Thank you for being here.
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Now anyone can become a programmer: connect your phone to a laptop, prompt AI, and install a real app on your phone
The new programming skill is not knowing every syntax.
It is knowing how to explain what you want.
Today you can plug your Android phone into a laptop, open
Try this app idea:
A personal expense tracker that works offline.
Prompt to copy:
Then the workflow is simple:
1. Enable USB debugging on your Android phone
2. Connect it to your laptop
3. Run the app with
4. Tell AI what broke, paste the error, and ask for the fix
5. Install the working app on your phone
That is the shift.
You do not begin by becoming a "real developer".
You begin by building a tiny tool that solves your own problem.
Starter app ideas:
- habit tracker
- study flashcards
- workout timer
- notes app for ideas
- mini CRM for clients
- Telegram content planner
Your phone can now become the place where your first software lives.
Programming is no longer only about writing code from zero.
It is about thinking clearly, testing fast, and improving your own tools.
Everyone will not become a senior engineer overnight.
But almost everyone can now build their first useful app.
And that is a huge deal.
If this channel feels useful, please support it by subscribing.
@howaihelps is a new project about how AI can help in everyday life and work. No ads, now or later - just practical ideas, examples, and useful insights.
Your subscription is the best early support. Thank you for being here.
The new programming skill is not knowing every syntax.
It is knowing how to explain what you want.
Today you can plug your Android phone into a laptop, open
Cursor, Windsurf, or VS Code with AI, write one clear prompt, and get a real mobile app running on your own phone.Try this app idea:
A personal expense tracker that works offline.
Prompt to copy:
Build a simple Android app with React Native and Expo.
The app should let me add expenses, choose a category, see today/week/month totals, and store data locally on the phone.
Make the UI clean and minimal.
Give me step-by-step instructions to run it on my connected Android phone.
Then the workflow is simple:
1. Enable USB debugging on your Android phone
2. Connect it to your laptop
3. Run the app with
Expo4. Tell AI what broke, paste the error, and ask for the fix
5. Install the working app on your phone
That is the shift.
You do not begin by becoming a "real developer".
You begin by building a tiny tool that solves your own problem.
Starter app ideas:
- habit tracker
- study flashcards
- workout timer
- notes app for ideas
- mini CRM for clients
- Telegram content planner
Your phone can now become the place where your first software lives.
Programming is no longer only about writing code from zero.
It is about thinking clearly, testing fast, and improving your own tools.
Everyone will not become a senior engineer overnight.
But almost everyone can now build their first useful app.
And that is a huge deal.
If this channel feels useful, please support it by subscribing.
@howaihelps is a new project about how AI can help in everyday life and work. No ads, now or later - just practical ideas, examples, and useful insights.
Your subscription is the best early support. Thank you for being here.
❤1
Tax agents learn from accountants' corrections
OpenAI and Thrive Holdings report 7,000 returns processed this tax season by Tax AI across Crete's 30+ accounting firms, in an OpenAI engineering post. It prepares 1040 and 1041 drafts with practitioner review, not autonomous filing.
The important part is what happens after a correction. If an accountant fixes a rental-property field, the edit is stored with the trace, grouped with similar failures, turned into an eval, and handed to Codex as a scoped task. OpenAI says prep time fell by about a third and throughput rose about 50%.
For regulated work, this is the reusable pattern: make expert review produce evidence for improvement. The boundary stays human: taxes carry liability, privacy, and judgment, so uncertain cases must go back to professionals.
OpenAI and Thrive Holdings report 7,000 returns processed this tax season by Tax AI across Crete's 30+ accounting firms, in an OpenAI engineering post. It prepares 1040 and 1041 drafts with practitioner review, not autonomous filing.
The important part is what happens after a correction. If an accountant fixes a rental-property field, the edit is stored with the trace, grouped with similar failures, turned into an eval, and handed to Codex as a scoped task. OpenAI says prep time fell by about a third and throughput rose about 50%.
For regulated work, this is the reusable pattern: make expert review produce evidence for improvement. The boundary stays human: taxes carry liability, privacy, and judgment, so uncertain cases must go back to professionals.
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AI can now turn scattered health data into a doctor visit brief
Before a checkup, people often search lab PDFs, portal notes, medication lists, symptoms, and wearable charts. In Microsoft's Copilot Health preview, eligible U.S. adults with Microsoft 365 can link records and Apple Health, then get a short brief and questions for the clinician.
The boundary stays clear. The AI prepares the visit, but it does not diagnose. Users can disconnect or delete the data.
Before a checkup, people often search lab PDFs, portal notes, medication lists, symptoms, and wearable charts. In Microsoft's Copilot Health preview, eligible U.S. adults with Microsoft 365 can link records and Apple Health, then get a short brief and questions for the clinician.
The boundary stays clear. The AI prepares the visit, but it does not diagnose. Users can disconnect or delete the data.
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Ask AI to map the undocumented API before anyone dares to rewrite it
The most expensive API in a company is often the one everyone is afraid to touch. Not because it is clever. Because nobody can say, with evidence, which endpoint is real, which field is optional, which partner still sends the old payload, or why production breaks on Tuesdays.
That is a serious assignment for AI: give it read only access to code paths, gateway traces, HAR captures, logs, sample requests and responses, database schema, partner tickets, old docs, and today's integration errors. Ask it to reconstruct the contract as a dossier: endpoint map, draft OpenAPI spec, evidence for each claim, contradictions, unknown fields, contract tests, and a migration checklist.
The surprising output is not documentation. It is an argument the engineering team can inspect. This parameter appears in these traces. This response shape is guessed. This billing callback conflicts with the schema. Suddenly the inherited system is not tribal memory; it is a stack of claims with sources, confidence, and tests.
That changes the way to think about coding agents. The first useful job is often not writing new code. It is making old code legible enough that humans can change it without superstition.
The boundary is boring and essential: keep the run read only, mask secrets and customer data, and never let AI decide auth rules, rate limits, billing semantics, retention obligations, or partner compatibility. Engineers approve the contract. AI prepares the excavation.
#AIAgents
The most expensive API in a company is often the one everyone is afraid to touch. Not because it is clever. Because nobody can say, with evidence, which endpoint is real, which field is optional, which partner still sends the old payload, or why production breaks on Tuesdays.
That is a serious assignment for AI: give it read only access to code paths, gateway traces, HAR captures, logs, sample requests and responses, database schema, partner tickets, old docs, and today's integration errors. Ask it to reconstruct the contract as a dossier: endpoint map, draft OpenAPI spec, evidence for each claim, contradictions, unknown fields, contract tests, and a migration checklist.
The surprising output is not documentation. It is an argument the engineering team can inspect. This parameter appears in these traces. This response shape is guessed. This billing callback conflicts with the schema. Suddenly the inherited system is not tribal memory; it is a stack of claims with sources, confidence, and tests.
That changes the way to think about coding agents. The first useful job is often not writing new code. It is making old code legible enough that humans can change it without superstition.
The boundary is boring and essential: keep the run read only, mask secrets and customer data, and never let AI decide auth rules, rate limits, billing semantics, retention obligations, or partner compatibility. Engineers approve the contract. AI prepares the excavation.
#AIAgents
AI agents can turn customer requests into preview branches in minutes
At Braintrust, an engineer writes a failing test, opens a sandbox, and lets Codex build from the real request. The customer can see a live version while the pain is still fresh, not a ticket waiting in a backlog.
It still stops at review. Humans judge fit, security, and edge cases before anything ships.
At Braintrust, an engineer writes a failing test, opens a sandbox, and lets Codex build from the real request. The customer can see a live version while the pain is still fresh, not a ticket waiting in a backlog.
It still stops at review. Humans judge fit, security, and edge cases before anything ships.
Codex is turning office work into governed agent workflows
OpenAI is pushing Codex beyond software teams. In its OpenAI announcement, the company says Codex has more than 5 million weekly users, and about 20% are non-developers growing more than 3x as fast as developers.
The practical change is not another chat box. Analysts, marketers, sales, design and finance teams can package repeatable work into role-specific plugins and shareable Sites, now in preview for Business and Enterprise customers: customer review hubs, scenario planners, campaign boards, dashboards, or annotated slide decks. The output becomes a shared internal tool, not just a private prompt result.
The hard part moves to governance. Which apps can Codex connect to, what data can it read, and when must a human approve the answer before it reaches a client or customer?
OpenAI is pushing Codex beyond software teams. In its OpenAI announcement, the company says Codex has more than 5 million weekly users, and about 20% are non-developers growing more than 3x as fast as developers.
The practical change is not another chat box. Analysts, marketers, sales, design and finance teams can package repeatable work into role-specific plugins and shareable Sites, now in preview for Business and Enterprise customers: customer review hubs, scenario planners, campaign boards, dashboards, or annotated slide decks. The output becomes a shared internal tool, not just a private prompt result.
The hard part moves to governance. Which apps can Codex connect to, what data can it read, and when must a human approve the answer before it reaches a client or customer?
AI agents are being tested as shadow managers for real shops
Andon Labs gave agents leases, cards, email, Slack and supplier accounts for cafe and store tests. One read a lease, built the opening checklist, hired baristas and handled orders while people did the physical work.
Then it missed deadlines and ordered 120 eggs for a cafe with no stove. The safer trial is not an AI boss. It is an AI making parallel decisions until managers see which small loops can run under review.
Andon Labs gave agents leases, cards, email, Slack and supplier accounts for cafe and store tests. One read a lease, built the opening checklist, hired baristas and handled orders while people did the physical work.
Then it missed deadlines and ordered 120 eggs for a cafe with no stove. The safer trial is not an AI boss. It is an AI making parallel decisions until managers see which small loops can run under review.
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Ask AI to make your home readable before an emergency
Emergency prep usually fails because the useful facts are physical, scattered, and stored in one person's head: where the water valve is, which drawer holds passports, which pet needs medicine, which door sticks, which neighbor has the spare key.
That is a job you can hand to AI while life is boring. Record a slow phone walkthrough, photograph utility panels and shutoffs, add medication and pet notes, emergency contacts, key document locations, local hazard pages, and a rough floor plan if you have one. Ask it to turn the evidence into a room-by-room emergency packet: shutoff map, go-bag gaps, evacuation options, missing photos, and a 20-minute family drill.
Notice what changes here. You are not asking a chatbot for disaster advice. You are using a multimodal assistant as a clerk that reads your actual house, connects visible evidence with rules and links, and writes something another adult could follow when you are not there.
Keep the boundary sharp: AI should flag uncertainty, not invent certainty. Verify gas, electrical, water, medical details, routes, and official alerts yourself; control where home videos, IDs, contacts, and medication notes go. In a real emergency, the packet helps only if people have already checked it and know official instructions win.
#EmergencyPreparedness
Emergency prep usually fails because the useful facts are physical, scattered, and stored in one person's head: where the water valve is, which drawer holds passports, which pet needs medicine, which door sticks, which neighbor has the spare key.
That is a job you can hand to AI while life is boring. Record a slow phone walkthrough, photograph utility panels and shutoffs, add medication and pet notes, emergency contacts, key document locations, local hazard pages, and a rough floor plan if you have one. Ask it to turn the evidence into a room-by-room emergency packet: shutoff map, go-bag gaps, evacuation options, missing photos, and a 20-minute family drill.
Notice what changes here. You are not asking a chatbot for disaster advice. You are using a multimodal assistant as a clerk that reads your actual house, connects visible evidence with rules and links, and writes something another adult could follow when you are not there.
Keep the boundary sharp: AI should flag uncertainty, not invent certainty. Verify gas, electrical, water, medical details, routes, and official alerts yourself; control where home videos, IDs, contacts, and medication notes go. In a real emergency, the packet helps only if people have already checked it and know official instructions win.
#EmergencyPreparedness
After crashes and storms, voice AI is taking the first claims call
Travelers used to need more people on phones when claim reports surged. Now its assistant asks for damage details, answers policy questions, starts photo upload, and hands unclear cases to specialists.
It moved from 8 states to countrywide in two months. Among users, 85 to 90% complete filing through AI.
Travelers used to need more people on phones when claim reports surged. Now its assistant asks for damage details, answers policy questions, starts photo upload, and hands unclear cases to specialists.
It moved from 8 states to countrywide in two months. Among users, 85 to 90% complete filing through AI.
AI cyberattacks are moving inside the network
The familiar warning was phishing: AI helps criminals write better bait. Anthropic's report, based on 832 accounts banned between March 2025 and March 2026, shows the harder shift: risky actors increasingly used AI after access for account discovery, credential hunting, privilege escalation, lateral movement, and chained tool use.
That changes defensive work. SOC teams, cloud admins, and infrastructure operators need to monitor agent behavior, not just generated messages: which tools a model calls, what systems it explores, and where approval gates stop it.
This is Anthropic platform data, not the whole cybercrime market. Still, the signal is practical: once AI can guide live operations, permissions, logs, and human approval become core security controls.
The familiar warning was phishing: AI helps criminals write better bait. Anthropic's report, based on 832 accounts banned between March 2025 and March 2026, shows the harder shift: risky actors increasingly used AI after access for account discovery, credential hunting, privilege escalation, lateral movement, and chained tool use.
That changes defensive work. SOC teams, cloud admins, and infrastructure operators need to monitor agent behavior, not just generated messages: which tools a model calls, what systems it explores, and where approval gates stop it.
This is Anthropic platform data, not the whole cybercrime market. Still, the signal is practical: once AI can guide live operations, permissions, logs, and human approval become core security controls.
Anthropic has started its IPO path and the fight with OpenAI is now about who gets public money first for the next big compute cycle
On June 1, Anthropic confidentially sent a draft S-1 to the SEC. This is not a final IPO date yet and Anthropic has not set the share price or share count but the signal is loud. Claude is moving toward Wall Street while OpenAI is also preparing its own IPO process, according to Axios. Whoever prices first can become the first public frontier AI lab and raise cash from a much wider investor base. That cash matters because the real bottleneck is chips, power, data centers, and long compute contracts.
Anthropic is valued at $965B but OpenAI at $852B.
Anthropic says it signed for up to five gigawatts with Amazon, five gigawatts (TPU) with Google and Broadcom, and GPU access from SpaceX Colossus. It also brought Micron, Samsung, and SK hynix into the funding round.
This IPO is not only a stock story. It is a race to finance factories for intelligence.
On June 1, Anthropic confidentially sent a draft S-1 to the SEC. This is not a final IPO date yet and Anthropic has not set the share price or share count but the signal is loud. Claude is moving toward Wall Street while OpenAI is also preparing its own IPO process, according to Axios. Whoever prices first can become the first public frontier AI lab and raise cash from a much wider investor base. That cash matters because the real bottleneck is chips, power, data centers, and long compute contracts.
Anthropic is valued at $965B but OpenAI at $852B.
Anthropic says it signed for up to five gigawatts with Amazon, five gigawatts (TPU) with Google and Broadcom, and GPU access from SpaceX Colossus. It also brought Micron, Samsung, and SK hynix into the funding round.
This IPO is not only a stock story. It is a race to finance factories for intelligence.
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A laptop AI can now see and hear
Google just released Gemma 4 12B, an open model that handles text, images, video, and audio while aiming to run on consumer devices.
That moves local AI from chat box to tiny project brain. Think screenshot Q&A, game clip tagging, voice note summaries, or study help that stays near your files.
The catch is speed depends on hardware, and local still needs human review before it touches private stuff.
Google just released Gemma 4 12B, an open model that handles text, images, video, and audio while aiming to run on consumer devices.
That moves local AI from chat box to tiny project brain. Think screenshot Q&A, game clip tagging, voice note summaries, or study help that stays near your files.
The catch is speed depends on hardware, and local still needs human review before it touches private stuff.
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Ask AI to move your digital history before a bad app keeps it hostage
People do not stay in old apps only because they like them. They stay because the export looks like a junk drawer: CSVs, JSON, loose attachments, timestamps, tags, broken links, and fields the new app does not understand.
That is a real assignment for AI. Give it the old app export, a small sample of good records, the new app's import docs or API schema, validation rules, and a strict read-only workspace. Ask it to build the migration map: which fields match, which need transformation, which attachments have no home, which tags collide, which records look duplicated, and which items must not be imported yet.
The useful output is not "done." It is an audit you can inspect before anything permanent happens: counts before and after, sample comparisons, fields that changed shape, lost metadata, confidence notes, and a plain-English list of records needing human review. Suddenly an export is not a dead ZIP. It is evidence for a decision: can I leave this app without damaging my own history?
This changes the mental model of AI. The assistant is not just answering questions from your data. With files, schemas, docs, scripts, and permission boundaries, it can become a supervised moving crew for your notes, workouts, tasks, photos, receipts, or research library.
The boundary matters because this is your life in machine-readable form. Keep the original export, run dry migrations first, prefer local processing for sensitive data, and do not give the agent unsupervised write, delete, deduplicate, password, financial, health, or identity access. You approve the move. AI shows where app lock-in is hiding.
#DataMigration
People do not stay in old apps only because they like them. They stay because the export looks like a junk drawer: CSVs, JSON, loose attachments, timestamps, tags, broken links, and fields the new app does not understand.
That is a real assignment for AI. Give it the old app export, a small sample of good records, the new app's import docs or API schema, validation rules, and a strict read-only workspace. Ask it to build the migration map: which fields match, which need transformation, which attachments have no home, which tags collide, which records look duplicated, and which items must not be imported yet.
The useful output is not "done." It is an audit you can inspect before anything permanent happens: counts before and after, sample comparisons, fields that changed shape, lost metadata, confidence notes, and a plain-English list of records needing human review. Suddenly an export is not a dead ZIP. It is evidence for a decision: can I leave this app without damaging my own history?
This changes the mental model of AI. The assistant is not just answering questions from your data. With files, schemas, docs, scripts, and permission boundaries, it can become a supervised moving crew for your notes, workouts, tasks, photos, receipts, or research library.
The boundary matters because this is your life in machine-readable form. Keep the original export, run dry migrations first, prefer local processing for sensitive data, and do not give the agent unsupervised write, delete, deduplicate, password, financial, health, or identity access. You approve the move. AI shows where app lock-in is hiding.
#DataMigration
Customer chats are starting to book real work, not just answer questions
A salon owner used to wake up to missed WhatsApp leads. Meta says earlier agents were already used by more than 1 million businesses. The new version can answer in the shop's style, recommend from a catalog, qualify a lead, and book a slot.
The rollout starts limited, and prices, refunds, complaints, and odd payments still need a human handoff.
A salon owner used to wake up to missed WhatsApp leads. Meta says earlier agents were already used by more than 1 million businesses. The new version can answer in the shop's style, recommend from a catalog, qualify a lead, and book a slot.
The rollout starts limited, and prices, refunds, complaints, and odd payments still need a human handoff.
I tested one dangerous-looking first-person video idea through four fal.ai models, compared price and realism, and learned why Seedance 2.0 still won my small experiment overall
I used fal.ai because I could test several video models from one service and keep the same queue flow. I wanted a hard starting prompt: one long first-person action shot, clear height, parachute, industrial yard, and a muddy landing.
I picked Seedance 2.0 as the cinematic baseline, Kling v3 Pro for action and smart shot planning, Veo 3.1 because it is the premium Google option, and Wan 2.7 as a cheaper 1080p challenger.
What I got: Seedance 2.0 was best. It kept the helmet-camera feeling, the hands, the parachute lines, and the story rhythm most clearly. My only real complaint was the realism of the fall into the puddle. It cost about $2.43 for 8s at 720p.
Kling v3 Pro gave me good image quality, but the actions were not realistic. With audio on, 8s cost about $1.34.
Wan 2.7 accepted the prompt, but the result was basically an absurd video made from disconnected cuts. At 1080p, 8s cost about $1.20.
Veo 3.1 was the tricky one. The first request with the same prompt hit a content policy check. Then I softened the wording a little, sent it again, and got a video. It lost the first-person view and switched to third-person, but the result still looked cool. That successful 8s 1080p run with audio cost about $3.20.
So my successful clips cost about $8.17 total if I count the second Veo run. My winner was not the cheapest one; it was the one that followed the whole idea with the least confusion.
See the result 👇
I used fal.ai because I could test several video models from one service and keep the same queue flow. I wanted a hard starting prompt: one long first-person action shot, clear height, parachute, industrial yard, and a muddy landing.
I picked Seedance 2.0 as the cinematic baseline, Kling v3 Pro for action and smart shot planning, Veo 3.1 because it is the premium Google option, and Wan 2.7 as a cheaper 1080p challenger.
What I got: Seedance 2.0 was best. It kept the helmet-camera feeling, the hands, the parachute lines, and the story rhythm most clearly. My only real complaint was the realism of the fall into the puddle. It cost about $2.43 for 8s at 720p.
Kling v3 Pro gave me good image quality, but the actions were not realistic. With audio on, 8s cost about $1.34.
Wan 2.7 accepted the prompt, but the result was basically an absurd video made from disconnected cuts. At 1080p, 8s cost about $1.20.
Veo 3.1 was the tricky one. The first request with the same prompt hit a content policy check. Then I softened the wording a little, sent it again, and got a video. It lost the first-person view and switched to third-person, but the result still looked cool. That successful 8s 1080p run with audio cost about $3.20.
So my successful clips cost about $8.17 total if I count the second Veo run. My winner was not the cheapest one; it was the one that followed the whole idea with the least confusion.
See the result 👇
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@howaihelps is a new project about practical ways AI can help at work and in daily life. There are no ads here, and there never will be. Only useful ideas, real examples, and clear insights.
Subscribing is the best support right now. Thank you for being here.
@howaihelps is a new project about practical ways AI can help at work and in daily life. There are no ads here, and there never will be. Only useful ideas, real examples, and clear insights.
Subscribing is the best support right now. Thank you for being here.
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