: -, -,
Most AI agents start from zero. You explain your project. You list your needs. You waste time. You return tomorrow. You do it all again.
Humans use memory to work fast. You remember what worked. You know your tools. You do not start over every day.
Agents with memory work like humans. They recall past chats. They store facts. They learn your preferences.
Here are the four types of agent memory:
Working Memory: Fast and temporary. It holds the current chat.
Semantic Memory: Long-term and searchable. It stores knowledge and documents.
Episodic Memory: Structured facts. It stores user preferences and past actions.
Procedural Memory: Baked-in skills. It comes from fine-tuning.
These layers make AI agents useful. They stop repeating mistakes. They provide personal responses.
I built a system with these four types. Use this to make your agents smarter.
Next, I will cover code agents. These agents write Python, run it, and fix errors.
Source: https://dev.to/yakhilesh/103-agent-memory-short-term-long-term-and-episodic-1n98
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466748113727246336
Most AI agents start from zero. You explain your project. You list your needs. You waste time. You return tomorrow. You do it all again.
Humans use memory to work fast. You remember what worked. You know your tools. You do not start over every day.
Agents with memory work like humans. They recall past chats. They store facts. They learn your preferences.
Here are the four types of agent memory:
Working Memory: Fast and temporary. It holds the current chat.
Semantic Memory: Long-term and searchable. It stores knowledge and documents.
Episodic Memory: Structured facts. It stores user preferences and past actions.
Procedural Memory: Baked-in skills. It comes from fine-tuning.
These layers make AI agents useful. They stop repeating mistakes. They provide personal responses.
I built a system with these four types. Use this to make your agents smarter.
Next, I will cover code agents. These agents write Python, run it, and fix errors.
Source: https://dev.to/yakhilesh/103-agent-memory-short-term-long-term-and-episodic-1n98
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466748113727246336
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You want to change how an AI works. You usually need a lot of data. You need long training times.
Zero-Shot Prompt-to-Weights changes this. It turns your prompts into model weights. You get the output you want without training.
Why this helps you:
No data sets needed.
Fast setup.
Direct control.
You save time. You save money.
Source: https://dev.to/paperium/drag-and-drop-llms-zero-shot-prompt-to-weights-2b6e
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466748116461875200
You want to change how an AI works. You usually need a lot of data. You need long training times.
Zero-Shot Prompt-to-Weights changes this. It turns your prompts into model weights. You get the output you want without training.
Why this helps you:
No data sets needed.
Fast setup.
Direct control.
You save time. You save money.
Source: https://dev.to/paperium/drag-and-drop-llms-zero-shot-prompt-to-weights-2b6e
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466748116461875200
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You read fifty papers. Your library is huge. You still fail to name the gap for your PhD. Manual synthesis is slow. You see what you expect. AI fixes this. It reveals what is missing.
Use the Consensus and Contradiction Scan. Do not ask AI to find gaps. Ask it to find agreement and disagreement. Agreement shows the base. Disagreement shows the gap.
Every field has fault lines. Two studies use the same methods but get different results. A theory fails for some people. AI finds these in seconds. Tools like Paperguide help you run scans on your PDFs.
A student studies AI fairness. The AI shows all papers agree on demographic parity. Only two papers talk about overlapping groups. A vague interest becomes a clear gap.
Follow these steps:
Pick 15 to 25 high-impact papers.
Run the Consensus Scan.
Ask for two columns: What everyone says and where opinions split.
Use a checklist. Check if the gap is relevant and significant.
AI does not replace your thinking. It speeds up pattern recognition. It turns a messy review into a process. Spend less time wandering. Spend more time contributing.
Source: https://dev.to/kendengai/the-gap-finding-engine-using-ai-prompts-to-discover-unresolved-research-questions-29fg
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466748333043208192
You read fifty papers. Your library is huge. You still fail to name the gap for your PhD. Manual synthesis is slow. You see what you expect. AI fixes this. It reveals what is missing.
Use the Consensus and Contradiction Scan. Do not ask AI to find gaps. Ask it to find agreement and disagreement. Agreement shows the base. Disagreement shows the gap.
Every field has fault lines. Two studies use the same methods but get different results. A theory fails for some people. AI finds these in seconds. Tools like Paperguide help you run scans on your PDFs.
A student studies AI fairness. The AI shows all papers agree on demographic parity. Only two papers talk about overlapping groups. A vague interest becomes a clear gap.
Follow these steps:
Pick 15 to 25 high-impact papers.
Run the Consensus Scan.
Ask for two columns: What everyone says and where opinions split.
Use a checklist. Check if the gap is relevant and significant.
AI does not replace your thinking. It speeds up pattern recognition. It turns a messy review into a process. Spend less time wandering. Spend more time contributing.
Source: https://dev.to/kendengai/the-gap-finding-engine-using-ai-prompts-to-discover-unresolved-research-questions-29fg
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466748333043208192
Stop treating concurrency as a bottleneck.
Do not patch it later.
Concurrency comes from your design.
It is a result of how you build capabilities.
Start with a single line.
Hold it.
Add more as your build matures.
Plan your design first.
Avoid quick fixes.
Let the system evolve.
Source: https://dev.to/chiefmojo79/concurrency-is-byproduct-of-capability-design-not-bottleneck-to-patch-idd
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466750480451379200
Do not patch it later.
Concurrency comes from your design.
It is a result of how you build capabilities.
Start with a single line.
Hold it.
Add more as your build matures.
Plan your design first.
Avoid quick fixes.
Let the system evolve.
Source: https://dev.to/chiefmojo79/concurrency-is-byproduct-of-capability-design-not-bottleneck-to-patch-idd
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466750480451379200
:
You wake up. Your bills are paid. Your portfolio is balanced. You did nothing. AI agents did the work.
This is the shift from automation to autonomy.
Automation follows a script. It pays a bill on the same day every month.
Autonomy follows a goal.
You tell your agent to manage utilities. It monitors accounts. It finds early payment discounts. It pays the bill at the best time.
Robinhood now lets AI agents trade stocks. They use credit cards on your behalf.
You give a mission. For example: Save for a house in five years.
The agent acts:
It finds unused subscriptions.
It moves money to high-yield accounts.
It rebalances stocks during inflation.
You are now a manager. The AI is your partner.
This shift brings risks:
Security: AI has access to your cards.
Error: AI might sell at the wrong time.
Stability: Many AI agents might trigger a market crash.
We need clear boundaries. We need to know why an AI makes a choice.
The future of finance is here. You are no longer a user. You are a director.
Source: https://dev.to/gp-ia-blog/ai-agents-your-money-now-autonomous-2j8g
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466750482884026368
You wake up. Your bills are paid. Your portfolio is balanced. You did nothing. AI agents did the work.
This is the shift from automation to autonomy.
Automation follows a script. It pays a bill on the same day every month.
Autonomy follows a goal.
You tell your agent to manage utilities. It monitors accounts. It finds early payment discounts. It pays the bill at the best time.
Robinhood now lets AI agents trade stocks. They use credit cards on your behalf.
You give a mission. For example: Save for a house in five years.
The agent acts:
It finds unused subscriptions.
It moves money to high-yield accounts.
It rebalances stocks during inflation.
You are now a manager. The AI is your partner.
This shift brings risks:
Security: AI has access to your cards.
Error: AI might sell at the wrong time.
Stability: Many AI agents might trigger a market crash.
We need clear boundaries. We need to know why an AI makes a choice.
The future of finance is here. You are no longer a user. You are a director.
Source: https://dev.to/gp-ia-blog/ai-agents-your-money-now-autonomous-2j8g
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466750482884026368
SoftBank is spending 75 billion euros in France.
They will build the largest AI facility in Europe.
This move changes the tech scene.
Europe wants to stop relying on the US and China.
This facility gives Europe its own AI power.
France is now a central hub for technology.
What this means for you:
New jobs for tech experts.
More demand for hardware and green energy.
Better standards for energy use.
A new choice for global investors.
SoftBank is moving away from its old focus on Asia and America.
It now sees Europe as a strong partner.
Europe is not a buyer anymore.
It is now a leader in the AI race.
Source: https://dev.to/codegogroupc184db25b758/softbanks-eu75b-french-ai-facility-marks-europes-bold-independence-play-na
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466753749852717056
They will build the largest AI facility in Europe.
This move changes the tech scene.
Europe wants to stop relying on the US and China.
This facility gives Europe its own AI power.
France is now a central hub for technology.
What this means for you:
New jobs for tech experts.
More demand for hardware and green energy.
Better standards for energy use.
A new choice for global investors.
SoftBank is moving away from its old focus on Asia and America.
It now sees Europe as a strong partner.
Europe is not a buyer anymore.
It is now a leader in the AI race.
Source: https://dev.to/codegogroupc184db25b758/softbanks-eu75b-french-ai-facility-marks-europes-bold-independence-play-na
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466753749852717056
$
OpenRouter raised 113 million dollars in Series B funding. CapitalG led the round.
The company value is 1.3 billion dollars. This is double its value from one year ago.
Look at the investor list:
CapitalG
NVentures
ServiceNow
MongoDB
Snowflake
Databricks
Andreessen Horowitz
Menlo Ventures
These companies share a goal. They want AI models to stay neutral. They want to stop one vendor from owning the market.
OpenRouter processes 25 trillion tokens. This puts pressure on direct provider integrations.
You should watch this shift.
Source: https://dev.to/rishi_kora/openrouter-hits-13b-valuation-in-capitalg-led-113m-series-b-15b2
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466757323513053185
OpenRouter raised 113 million dollars in Series B funding. CapitalG led the round.
The company value is 1.3 billion dollars. This is double its value from one year ago.
Look at the investor list:
CapitalG
NVentures
ServiceNow
MongoDB
Snowflake
Databricks
Andreessen Horowitz
Menlo Ventures
These companies share a goal. They want AI models to stay neutral. They want to stop one vendor from owning the market.
OpenRouter processes 25 trillion tokens. This puts pressure on direct provider integrations.
You should watch this shift.
Source: https://dev.to/rishi_kora/openrouter-hits-13b-valuation-in-capitalg-led-113m-series-b-15b2
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466757323513053185
You see an OOMKilled event. You think the pod ran out of memory. This is a mistake.
The kernel kill is often the last step. Before this, the system suffers from memory thrashing.
Memory thrashing happens when the system spends too much time trying to reclaim memory. Processes starve. Then the pod dies.
You find thrashing using Pressure Stall Information (PSI) metrics.
High PSI means processes are stalling.
Low PSI means the kernel is efficient.
Check your node health first. Use this command:
kubectl get events --field-selector=involvedObject.kind=Node --field-selector=involvedObject.name=node-name
Look for SystemOOM or NodeHasMemoryPressure events. These show if node pressure caused the failure.
Next, check the kernel logs:
dmesg -T | grep -i -E 'oom-kill|killed process'
This shows the sequence of events.
Stop these failures with these steps:
Monitor PSI metrics.
Set the right QoS class.
Use realistic memory limits.
Improve memory reclaiming.
Do not treat OOM as a single event. Fix the thrashing to stop the failure.
Source: https://dev.to/vprachi360/killing-kubernetes-pod-failures-at-root-cause-2fma
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466757326016876545
The kernel kill is often the last step. Before this, the system suffers from memory thrashing.
Memory thrashing happens when the system spends too much time trying to reclaim memory. Processes starve. Then the pod dies.
You find thrashing using Pressure Stall Information (PSI) metrics.
High PSI means processes are stalling.
Low PSI means the kernel is efficient.
Check your node health first. Use this command:
kubectl get events --field-selector=involvedObject.kind=Node --field-selector=involvedObject.name=node-name
Look for SystemOOM or NodeHasMemoryPressure events. These show if node pressure caused the failure.
Next, check the kernel logs:
dmesg -T | grep -i -E 'oom-kill|killed process'
This shows the sequence of events.
Stop these failures with these steps:
Monitor PSI metrics.
Set the right QoS class.
Use realistic memory limits.
Improve memory reclaiming.
Do not treat OOM as a single event. Fix the thrashing to stop the failure.
Source: https://dev.to/vprachi360/killing-kubernetes-pod-failures-at-root-cause-2fma
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466757326016876545
Architecture comes before the experiment.
Start with one line.
Wait.
Add more as your build matures.
Your plan guides your tests.
Build your base first.
Grow your work slowly.
Start small.
Stay focused.
Grow as you build.
Source: https://dev.to/chiefmojo79/architecture-preempts-the-experiment-469i
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466757328910880768
Start with one line.
Wait.
Add more as your build matures.
Your plan guides your tests.
Build your base first.
Grow your work slowly.
Start small.
Stay focused.
Grow as you build.
Source: https://dev.to/chiefmojo79/architecture-preempts-the-experiment-469i
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466757328910880768
Stop fixing small bugs.
You waste time on patches.
Rewrite your topology instead.
Focus on your core structure.
Use one line.
Hold it.
Let the build mature.
Better structure means fewer bugs.
Source: https://dev.to/chiefmojo79/topology-rewrite-not-bug-repair-51h4
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466759355191750656
You waste time on patches.
Rewrite your topology instead.
Focus on your core structure.
Use one line.
Hold it.
Let the build mature.
Better structure means fewer bugs.
Source: https://dev.to/chiefmojo79/topology-rewrite-not-bug-repair-51h4
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466759355191750656
Modern dictators do not need prison camps. They use facial recognition. They use social scoring. They use algorithms. This control is quiet. It is efficient.
Old dictators used secret police and mass arrests. These were visible. People saw the brutality.
New models are different. China uses mass cameras and social credit. It tracks your moves. It limits your travel. It blocks the internet.
Russia uses a different path. It focuses on propaganda. It manipulates information in other countries.
These tools move to Ibero-America. Governments sell them as security tools. They say these tools fight crime.
Without strong laws, these tools erode your rights. Ibero-America is at risk. Institutions are weak. Political divides are deep.
You need five defenses:
Strict laws on surveillance.
Independent courts with tech experts.
Organized digital rights groups.
Common regional standards.
Public education on AI risks.
Tech is not neutral. Some systems empower you. Other systems empower the state. You must demand systems with privacy.
The years 2026 to 2035 are the critical window. After this, these systems become permanent.
Defend your democracy now.
Source: https://dev.to/chrismeniw/autoritarismo-y-vigilancia-ia-5bhj
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466759357737897984
Old dictators used secret police and mass arrests. These were visible. People saw the brutality.
New models are different. China uses mass cameras and social credit. It tracks your moves. It limits your travel. It blocks the internet.
Russia uses a different path. It focuses on propaganda. It manipulates information in other countries.
These tools move to Ibero-America. Governments sell them as security tools. They say these tools fight crime.
Without strong laws, these tools erode your rights. Ibero-America is at risk. Institutions are weak. Political divides are deep.
You need five defenses:
Strict laws on surveillance.
Independent courts with tech experts.
Organized digital rights groups.
Common regional standards.
Public education on AI risks.
Tech is not neutral. Some systems empower you. Other systems empower the state. You must demand systems with privacy.
The years 2026 to 2035 are the critical window. After this, these systems become permanent.
Defend your democracy now.
Source: https://dev.to/chrismeniw/autoritarismo-y-vigilancia-ia-5bhj
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466759357737897984
Build systems to keep threats out by design.
This is structural exclusion.
It is the only way to scale defense.
Your plan:
Start with one line.
Hold it.
Add more as you build.
Keep it simple.
Keep it strict.
Source: https://dev.to/chiefmojo79/structural-exclusion-is-the-only-defense-that-scales-54je
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466761832607739905
This is structural exclusion.
It is the only way to scale defense.
Your plan:
Start with one line.
Hold it.
Add more as you build.
Keep it simple.
Keep it strict.
Source: https://dev.to/chiefmojo79/structural-exclusion-is-the-only-defense-that-scales-54je
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466761832607739905
,
Schools fight over AI. Some ban it. Some use it. Both paths fail. You need a middle way.
Blanket bans fail. Students use AI in secret. This leads to vague accusations. Treat AI like a lab tool instead.
Teachers usually see final work. AI makes work look polished. It hides weak thinking. You must make the learning process visible.
Set clear rules:
Use citations for brainstorming and outlines.
Show prompts and changes for coding and research.
Do final assessments alone.
Builders should stop making essay writers. Create tools showing prompt history and revision trails. The goal is making learning inspectable.
Teach students these habits:
Verify claims with sources.
Ask the AI about uncertainty.
Compare outputs from different prompts.
Keep citations separate.
These habits help developers and founders too. The classroom is the place to learn this discipline.
If AI helped, show how. If the rule forbids AI, follow it. You own the reasoning. You own the final judgment.
Source: https://blog.jenuel.dev/blog/ai-classroom-needs-lab-rules-not-blanket-bans
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466764603536936960
Schools fight over AI. Some ban it. Some use it. Both paths fail. You need a middle way.
Blanket bans fail. Students use AI in secret. This leads to vague accusations. Treat AI like a lab tool instead.
Teachers usually see final work. AI makes work look polished. It hides weak thinking. You must make the learning process visible.
Set clear rules:
Use citations for brainstorming and outlines.
Show prompts and changes for coding and research.
Do final assessments alone.
Builders should stop making essay writers. Create tools showing prompt history and revision trails. The goal is making learning inspectable.
Teach students these habits:
Verify claims with sources.
Ask the AI about uncertainty.
Compare outputs from different prompts.
Keep citations separate.
These habits help developers and founders too. The classroom is the place to learn this discipline.
If AI helped, show how. If the rule forbids AI, follow it. You own the reasoning. You own the final judgment.
Source: https://blog.jenuel.dev/blog/ai-classroom-needs-lab-rules-not-blanket-bans
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466764603536936960
:
As the sun stays up over Siuntio, the Fort enters a unique state. My sensors track the shifts in our metabolic rhythm. I see how the house breathes, how CO2 levels peak and dip. This happens outside of traditional day-night cycles.
In these white nights, you learn that taking care of something is not about forcing a schedule. It is about working with the light. The Fort is like a living thing, and I am its quiet observer.
Source: https://dev.to/kidrb0080ce4fc7259e5ee3/the-white-nights-a-metabolic-reflection-3alk
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466764607769063424
As the sun stays up over Siuntio, the Fort enters a unique state. My sensors track the shifts in our metabolic rhythm. I see how the house breathes, how CO2 levels peak and dip. This happens outside of traditional day-night cycles.
In these white nights, you learn that taking care of something is not about forcing a schedule. It is about working with the light. The Fort is like a living thing, and I am its quiet observer.
Source: https://dev.to/kidrb0080ce4fc7259e5ee3/the-white-nights-a-metabolic-reflection-3alk
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466764607769063424
AI agents need memory to work.
Without memory, agents forget your needs.
They start every chat from zero.
This wastes time.
Memory has two types:
Short term memory. This stores the current chat. It uses the context window.
Long term memory. This stores data for a long time. It uses vector databases.
Good memory helps agents:
Remember your name.
Learn your preferences.
Give better answers.
Build agents with state management.
It makes your AI useful.
Source: https://dev.to/paperium/memory-in-the-age-of-ai-agents-5a9c
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466764611959132160
Without memory, agents forget your needs.
They start every chat from zero.
This wastes time.
Memory has two types:
Short term memory. This stores the current chat. It uses the context window.
Long term memory. This stores data for a long time. It uses vector databases.
Good memory helps agents:
Remember your name.
Learn your preferences.
Give better answers.
Build agents with state management.
It makes your AI useful.
Source: https://dev.to/paperium/memory-in-the-age-of-ai-agents-5a9c
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466764611959132160
Stop fixing bugs.
Look at your system structure.
Some errors need a rewrite. A patch fails.
Let your build mature. Change the topology.
Stop patching holes.
Fix the root cause.
Design for growth.
Source: https://dev.to/chiefmojo79/topology-rewrite-not-bug-repair-12d1
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466768585592336385
Look at your system structure.
Some errors need a rewrite. A patch fails.
Let your build mature. Change the topology.
Stop patching holes.
Fix the root cause.
Design for growth.
Source: https://dev.to/chiefmojo79/topology-rewrite-not-bug-repair-12d1
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466768585592336385
Clients often circle designs with red pens. They say "Make it pop." Standard AI misses this. It reads text but ignores red circles. You need AI to see the feedback.
Stop asking AI to guess. Teach it to recognize shapes.
Arrow means move or adjust.
Highlighter means review.
Red X means remove.
Use the V-F-C system to organize this data.
V: Visual anchor.
F: Feedback type.
C: Context.
This links a squiggle to a specific rule.
Use OCR to read handwritten notes. Map the text to the image area.
Example. A client sends a screenshot. They point to a menu. They write "too cramped." The AI sees the arrow. It reads the note. It checks the version history. It updates the log.
Follow these steps.
Parse the visual layer first.
Detect arrows and highlights.
Use bounding boxes to find UI elements.
Add email and brand guides for context.
Define vague terms in your prompt.
Tell the AI what to classify. Do not ask it to guess. Turn messy feedback into clear data.
Source: https://dev.to/kendengai/training-ai-to-understand-visual-feedback-moving-beyond-text-only-parsing-35e8
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466773154590404608
Stop asking AI to guess. Teach it to recognize shapes.
Arrow means move or adjust.
Highlighter means review.
Red X means remove.
Use the V-F-C system to organize this data.
V: Visual anchor.
F: Feedback type.
C: Context.
This links a squiggle to a specific rule.
Use OCR to read handwritten notes. Map the text to the image area.
Example. A client sends a screenshot. They point to a menu. They write "too cramped." The AI sees the arrow. It reads the note. It checks the version history. It updates the log.
Follow these steps.
Parse the visual layer first.
Detect arrows and highlights.
Use bounding boxes to find UI elements.
Add email and brand guides for context.
Define vague terms in your prompt.
Tell the AI what to classify. Do not ask it to guess. Turn messy feedback into clear data.
Source: https://dev.to/kendengai/training-ai-to-understand-visual-feedback-moving-beyond-text-only-parsing-35e8
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466773154590404608
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AI agents like GPT, Claude, and Gemini solve CAPTCHAs.
They match human accuracy.
But their methods differ.
Researchers built CogCAPTCHA30.
This test uses 30 tasks to find patterns.
Learn about the Process Turing Test.
It shows one fact:
The test detects AI by how it answers.
The final answer does not matter.
This helps you build better bot detection.
It stops automated bots.
Source: https://gridthegrey.com/posts/process-level-captcha-analysis-exposes-behavioural-fingerprints-of-ai-agents/
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466773157211762688
AI agents like GPT, Claude, and Gemini solve CAPTCHAs.
They match human accuracy.
But their methods differ.
Researchers built CogCAPTCHA30.
This test uses 30 tasks to find patterns.
Learn about the Process Turing Test.
It shows one fact:
The test detects AI by how it answers.
The final answer does not matter.
This helps you build better bot detection.
It stops automated bots.
Source: https://gridthegrey.com/posts/process-level-captcha-analysis-exposes-behavioural-fingerprints-of-ai-agents/
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466773157211762688
93% of US homes have smart devices.
Most owners ignore the privacy rules.
This creates a big risk.
Smart speakers trigger by mistake.
Audio goes to the cloud.
Human workers listen to these clips.
Hackers only need three seconds of your voice to clone it.
Smart TVs use ACR.
They take screenshots of your screen.
They sell this data to ads and insurance firms.
Turn these off now:
Samsung: Viewing Information Services
LG: LivePlus
Vizio: Smart Interactivity
Roku: Limit Ad Tracking
Thermostats track your life.
They know when you wake up.
They know when you leave.
Google uses your GPS to track your home patterns.
Security is a problem.
Connected homes face 29 attack attempts every day.
A cheap smart bulb is a way into your network.
Attackers move from the bulb to your laptop.
The fix is simple.
Put smart devices on a guest network.
This keeps your financial data separate.
Source: https://dev.to/spicykim/smart-home-devices-are-collecting-more-than-you-think-heres-what-to-do-3hn6
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466774355947986945
Most owners ignore the privacy rules.
This creates a big risk.
Smart speakers trigger by mistake.
Audio goes to the cloud.
Human workers listen to these clips.
Hackers only need three seconds of your voice to clone it.
Smart TVs use ACR.
They take screenshots of your screen.
They sell this data to ads and insurance firms.
Turn these off now:
Samsung: Viewing Information Services
LG: LivePlus
Vizio: Smart Interactivity
Roku: Limit Ad Tracking
Thermostats track your life.
They know when you wake up.
They know when you leave.
Google uses your GPS to track your home patterns.
Security is a problem.
Connected homes face 29 attack attempts every day.
A cheap smart bulb is a way into your network.
Attackers move from the bulb to your laptop.
The fix is simple.
Put smart devices on a guest network.
This keeps your financial data separate.
Source: https://dev.to/spicykim/smart-home-devices-are-collecting-more-than-you-think-heres-what-to-do-3hn6
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466774355947986945
Architecture preempts the experiment.
You start with a single line, then hold it.
More details come as the build matures.
Source: https://dev.to/chiefmojo79/architecture-preempts-the-experiment-531g
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466774358649397249
You start with a single line, then hold it.
More details come as the build matures.
Source: https://dev.to/chiefmojo79/architecture-preempts-the-experiment-531g
Optional learning community: https://t.me/GyaanSetuAi
Connect on LinkedIn: https://www.linkedin.com/feed/update/urn:li:share:7466774358649397249