AI Lab
@AISystemAgentLab - Канал Telegram
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AI Lab — Telegram-канал на тему Разное, более 5k подписчиков. Practical AI workflows, agents and automation systems for people, founders and businesses. No hype. Just useful systems. Buy ads: https://telega.io/c/AISystemAgentLab ADS: CITYTRAVEL (Flight ticket... Подпишитесь на канал, чтобы получать все обновления прямо в Telegram.
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Practical AI workflows, agents and automation systems for people, founders and businesses. No hype. Just useful systems. Buy ads: https://telega.io/c/AISystemAgentLab ADS: CITYTRAVEL (Flight tickets) https://shp.pub/7c3sd1?erid=2SDnjeJxX1C
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Anthropic just showed where practical AI is going next
Anthropic has launched **Claude Science**, a research workbench for scientific discovery.
The signal is not only pharma.
AI is moving from chat answers to research systems.
A useful AI research workflow:
1. collect sources
2. extract facts
3. compare options
4. generate hypotheses
5. build a review pack
6. human makes the decision
This pattern
Before you trust any AI research, ask for this
AI can make weak research look very confident.
So if you use AI for market research, competitors, customer feedback or business decisions, do not ask only:
“What is the answer?”
Ask for a **trust package**:
1. source list
2. fact vs interpretation
3. confidence level
4. conflicting evidence
5. unknowns
6. decision impact
7. next verification step
Use
Do not automate this with AI first
AI is powerful.
But the fastest way to get disappointed is to automate the wrong task first.
Here is the AI automation stop list:
1. angry customer replies
2. legal, finance or medical decisions
3. messy processes nobody understands
4. irreversible actions
5. one-time tasks
6. tasks with no success criteria
What should you automate first?
Look for tasks that are:
Stop losing decisions in chats
Most teams do not have an AI problem.
They have a decision memory problem.
Important decisions are scattered across chats, calls, voice notes, emails and random docs.
Then nobody remembers:
Who decided it?
Why did we choose it?
What was rejected?
What should happen next?
Build an **AI Decision Log**.
The system:
1. collect messy input
2. extract decisions
3. capture
Make AI stop starting from zero
Most people use AI like this:
open chat -> explain everything again -> get a generic answer -> repeat tomorrow.
The fix:
create your **Personal AI Context File**.
Save it as:
`AI_CONTEXT.md`
Put inside:
1. who you are
2. your goals
3. your tools
4. your constraints
5. your working style
6. your decision rules
7. your recurring tasks
8. what AI should not do
Use this
Your AI should not improvise every repeated task
If you ask AI to do the same work every week, but explain it from scratch every time, you are wasting the best part of AI.
Build a small **Personal AI SOP Library**.
SOP means:
a repeatable instruction for a task you do often.
Create one file per repeated task:
- `weekly_report_sop.md`
- `customer_reply_sop.md`
- `content_research_sop.md`
- `competi
The first AI answer is not the final answer
One of the biggest mistakes people make with AI:
they treat the first response as the result.
But the first response is usually just a raw draft.
Use this 5-step feedback loop:
1. draft
2. critique
3. improve
4. verify
5. finalize
Prompt to use after any first draft:
“Review your answer like a strict editor.
Find weak points, missing context, vague claim
Before you automate with AI, choose its error budget.
Not every task deserves the same level of trust. A rough brainstorm can be wrong. A customer message, payment or production change cannot.
Use this 4-level map:
20%: AI can move fast - ideas, summaries, first drafts.
5%: AI prepares, you sample-check - research, calendars, cleanup.
1%: AI drafts, you approve every result - customer replies, pub
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