uncontract retweeted
🥳 Week 39’s Product of the Week goes to Post by FreeSurf!
A truly free, open‑source scheduler that lets you queue up posts across platforms – no hidden fees.
Dive in: wonderlaunch.app/p/post-by-free…
@un_contract
A truly free, open‑source scheduler that lets you queue up posts across platforms – no hidden fees.
Dive in: wonderlaunch.app/p/post-by-free…
@un_contract
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See below for curated tweets covering advice around SEO over the past week. I try to cut through the noise for people to isolate worthwhile advice. If you would like to subscribe to my newsletter, please check my bio.
"Traditional SEO still works"
@SEOKeval mentions that traditional SEO is still working for his clients ie no focus on PR or listicles. The basics are still working including optimizing keywords, but especially backlinks seem to be an underlying feature that helps websites standout irrespective of AI fixation.
Source: x.com/SEOKeval/statu…
"How to automate SEO with Opus 5.5 (Full Course)"
While I debated listing this given that automating SEO is usually not a good idea, still there are innovative takes on the matter. He mentions Viktor (and perhaps is financially invested in the project), which can ingest data from several sources including Google Search console, DataforSEO and Ahrefs to drill down on SEO strategies, isolate what is doing well and isolate backlink strategies base on competitor information. Not a bad read not just for the agent capabilities but the different SEO tools one can use to get a birdseye view of how one is doing and strategy to implement from an agent.
Source: x.com/EXM7777/status…
"Design doesn't fix SEO problems"
@therobertmay explains how a site redesign is not a solution to getting more customers ultimately. SEO is more important for driving customers because just because a site looks better doesn't mean it performs better if the fundamentals of SEO are incorporated.
Source: x.com/therobertmay/s…
"SEO basics still important"
@bunbeau suggests to not focus too much on recent trends in engineering for AI overviews, but instead focus on basic things like whether your website pages are indexed and long-tail keywords.
Source: x.com/bunbeau/status…
For my newsletter following different topics for start ups including AI, SEO and Social Media Marketing, subscribe to my Feedfree Digest newsletter
*this was posted with FreeSurf's Post, a free social media poster with an API (X api usage paid at cost)
"Traditional SEO still works"
@SEOKeval mentions that traditional SEO is still working for his clients ie no focus on PR or listicles. The basics are still working including optimizing keywords, but especially backlinks seem to be an underlying feature that helps websites standout irrespective of AI fixation.
Source: x.com/SEOKeval/statu…
"How to automate SEO with Opus 5.5 (Full Course)"
While I debated listing this given that automating SEO is usually not a good idea, still there are innovative takes on the matter. He mentions Viktor (and perhaps is financially invested in the project), which can ingest data from several sources including Google Search console, DataforSEO and Ahrefs to drill down on SEO strategies, isolate what is doing well and isolate backlink strategies base on competitor information. Not a bad read not just for the agent capabilities but the different SEO tools one can use to get a birdseye view of how one is doing and strategy to implement from an agent.
Source: x.com/EXM7777/status…
"Design doesn't fix SEO problems"
@therobertmay explains how a site redesign is not a solution to getting more customers ultimately. SEO is more important for driving customers because just because a site looks better doesn't mean it performs better if the fundamentals of SEO are incorporated.
Source: x.com/therobertmay/s…
"SEO basics still important"
@bunbeau suggests to not focus too much on recent trends in engineering for AI overviews, but instead focus on basic things like whether your website pages are indexed and long-tail keywords.
Source: x.com/bunbeau/status…
For my newsletter following different topics for start ups including AI, SEO and Social Media Marketing, subscribe to my Feedfree Digest newsletter
*this was posted with FreeSurf's Post, a free social media poster with an API (X api usage paid at cost)
"SEO is dead in 2026."
People love repeating this narrative. "Google is broken, AI traffic ate search, don't bother."
They miss the entire point of distribution.
Here is why organic search is still the ultimate leverage leverage engine (and why I keep building for it):
When digital assets, fast feedback from organic search is your best signal.
Last Sunday, I put a brand new project online.
No domain authority. No backlinks. Zero marketing budget.
Today: First organic click directly from Google.
(thanks DataFast by @marclou)
1 single click.
Looks tiny on paper?
Look at the mechanics instead.
Most founders wait months to test indexing, architecture, and intent. They build giant SEO strategies before validating if Google even cares about their core offer.
That's the slow, painful way.
In a lean framework, that first click isn't just traffic. It's validation:
Indexation velocity is alive.
The site architecture works.
The intent signal is strong enough to bypass the noise.
If I tried to buy that intent via ads, I’d burn cash testing unproven keywords. If I relied purely on social posts, the traffic would die in 24 hours.
Over-analyzing algorithm updates before getting indexed is the ultimate founder trap.
You have two valid paths today:
1. The Passive Cynic Strategy:
Complain about GEO, AI Overviews, and decaying organic search. Spend weeks arguing on X while building nothing.
2. The Pure Velocity Strategy:
Ship clean architecture, push instantly, and capture long-tail intent before anyone else notices.
The fatal mistake is believing the doom-scrolling over execution.
Don't build complex content machines for unvalidated ideas.
Ship clean.
Let Google discover it.
When search volume picks up, optimize for scale.
Until then, speed > theories.
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See below for curated tweets covering advice around social media marketing. I try to cut through the noise for people to isolate worthwhile advice. If you would like to subscribe to my newsletter, please check my bio.
"Youtube's automated deactivation problem"
@TierlistMan39 along with others on youtube are facing increasing challenges from opaque algorithms that will demonetize channels individuals have presumably done significant work for, forcing many to speak out about the problem. A warning about some of the difficulties creating a sustainable income from youtube.
Source: x.com/TierlistMan39/…
"Importance of a social presence"
@anulagarwal mentions the benefit of developing a social presence can change one's career trajectory and open doors even if some posts many people don't care about, it's still worth the long term effort.
Source: x.com/anulagarwal/st…
"Are LinkedIn newsletters the easiest way to grow your list?"
@jayclouse discusses a case study of the Vojtech newsletter which relied on Linkedin's newsletter functionality to build his subscribers for his own list of subscribers in Kit. A key lesson being how social media distribution can help one find subscribers for newsletters.
Source: x.com/jayclouse/stat…
"Stay within legal lanes with youtube for success"
@robertoblake mentions youtube can still be a great place to grow an audience despite difficulties mentioned from other creators as long as you follow basic common sense legal rules like copyright
Source: x.com/robertoblake/s…
"Platform burnout"
@Boldyboy1975 mentions that while on the surface, platforms look to reward originality rather than clipped or borrowed content or AI content, then reality is much more complex, with burned out creators, low or slow pay and lack of clarity.
Source: x.com/Boldyboy1975/s…
For my newsletter following different topics for start ups including AI, SEO and Social Media Marketing, subscribe to my Feedfree Digest newsletter
*this was posted with FreeSurf's Post, a free social media poster with an API (X api usage paid at cost)
"Youtube's automated deactivation problem"
@TierlistMan39 along with others on youtube are facing increasing challenges from opaque algorithms that will demonetize channels individuals have presumably done significant work for, forcing many to speak out about the problem. A warning about some of the difficulties creating a sustainable income from youtube.
Source: x.com/TierlistMan39/…
"Importance of a social presence"
@anulagarwal mentions the benefit of developing a social presence can change one's career trajectory and open doors even if some posts many people don't care about, it's still worth the long term effort.
Source: x.com/anulagarwal/st…
"Are LinkedIn newsletters the easiest way to grow your list?"
@jayclouse discusses a case study of the Vojtech newsletter which relied on Linkedin's newsletter functionality to build his subscribers for his own list of subscribers in Kit. A key lesson being how social media distribution can help one find subscribers for newsletters.
Source: x.com/jayclouse/stat…
"Stay within legal lanes with youtube for success"
@robertoblake mentions youtube can still be a great place to grow an audience despite difficulties mentioned from other creators as long as you follow basic common sense legal rules like copyright
Source: x.com/robertoblake/s…
"Platform burnout"
@Boldyboy1975 mentions that while on the surface, platforms look to reward originality rather than clipped or borrowed content or AI content, then reality is much more complex, with burned out creators, low or slow pay and lack of clarity.
Source: x.com/Boldyboy1975/s…
For my newsletter following different topics for start ups including AI, SEO and Social Media Marketing, subscribe to my Feedfree Digest newsletter
*this was posted with FreeSurf's Post, a free social media poster with an API (X api usage paid at cost)
Platforms spent years begging for original content creators.
Now they’re finding out what that actually costs them — and what creators actually get in return.
The pitch was simple: make something yours. Don’t clip. Don’t stitch. Don’t farm. Film it, write it, own it. We’ll reward t is that “original” became a slogan, not a paycheck.
What they sold:
• “Original content is king.”
• “We’re raising the bar for quality.”
• “Creators who make real work will get paid.”
• New funds, new “original content rewards,” higher Partner Program thresholds, crackdowns on AI slop and clip accounts.
What creators actually got:
• Median earnings still sitting around a few thousand dollars a year for a huge share of people doing this full-time. Nearly half still under $10K. gigapay.com
• Money concentrated at the top. The top 10% take most of the brand spend. The middle gets squeezed between mega-stars and cheap UGC. scalablepod.com
• Platforms raising the gate. YouTube doubling watch-hour and Shorts thresholds. X killing old revenue share and replacing it with a narrower “original content” program. TikTok’s rewards system flagging actual original films as “unoriginal” until someone makes enough noise. creatorspotlight.com
• Payouts that look generous in a keynote and tiny in a bank account. A million views on some short-form programs is still lunch money. Brand deals became the real income — which means you’re not a creator, you’re a freelance ad unit with extra steps. malyly.com
• Burnout and payment chaos as the background noise: delayed invoices, 120-day waits, algorithm changes that wipe a month of work overnight.
The joke is the platforms do want original work. They just want it cheap, abundant, and fully owned by the feed.
Original creators take the risk: time, gear, ideas, reputation. Platforms take the audience, the data, and most of the ad money. Then they change the rules and call it “quality.”
So the fallout isn’t that original content failed. It’s that the people making it finally noticed the contract.
They were invited in as talent. They’re treated as inventory.
That’s the gap. And it’s why a lot of the smartest original creators are quietly building email lists, products, memberships, and businesses that don’t vanish when a platform redefines “original” next quarter., I’m on holiday Back on Spaces next week, here for the laughs love you all xxx
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See below for curated tweets covering advice around AI. I try to cut through the noise for people to isolate worthwhile advice. If you would like to subscribe to my newsletter, please check my bio.
"Break open-source AI tools for learning"
@LearnWithBrij recommends instead of trying to learn about AI from tutorials, try immersing yourself in openly available tools like OpenClaw, n8n and Ollama (as well as others) to try integrating them into your workflow, breaking them and learning how to be an AI engineer in the process.
Source: x.com/LearnWithBrij/…
"Try a suite of open-source tools to build better AI agents"
@damkina7 has an interesting write up showcasing lesser known open-source tools to fine-tune your own custom AI-agents or potential AI "employees" (tools include Storm, Agno and Cline)
Source: x.com/damkina7/statu…
"Try the open-source reverse-skill for better security with AI"
@bigaiguy His tweet showcases a rising security functionality that one can integrate with different AI agents to help arbitrage security problems, called reverse-skill. Worth taking a look perhaps for security work.
Source: x.com/bigaiguy/statu…
"Build agent failure write-ups"
@gadi_neelesh recommends making logs of agent failures when updating your codebase rather than focusing on agent benchmarks. Makes sense especially if agents are built on fixing patterns but don't have context of your codebase complexities. Give them a roadmap for the wrong way of doing things so they can learn from it and do better in the future.
Source: x.com/gadi_neelesh/s…
For my newsletter following different topics for start ups including AI, SEO and Social Media Marketing, subscribe to my Feedfree Digest newsletter
*this was posted with FreeSurf's Post, a free social media poster with an API (X api usage paid at cost)
"Break open-source AI tools for learning"
@LearnWithBrij recommends instead of trying to learn about AI from tutorials, try immersing yourself in openly available tools like OpenClaw, n8n and Ollama (as well as others) to try integrating them into your workflow, breaking them and learning how to be an AI engineer in the process.
Source: x.com/LearnWithBrij/…
"Try a suite of open-source tools to build better AI agents"
@damkina7 has an interesting write up showcasing lesser known open-source tools to fine-tune your own custom AI-agents or potential AI "employees" (tools include Storm, Agno and Cline)
Source: x.com/damkina7/statu…
"Try the open-source reverse-skill for better security with AI"
@bigaiguy His tweet showcases a rising security functionality that one can integrate with different AI agents to help arbitrage security problems, called reverse-skill. Worth taking a look perhaps for security work.
Source: x.com/bigaiguy/statu…
"Build agent failure write-ups"
@gadi_neelesh recommends making logs of agent failures when updating your codebase rather than focusing on agent benchmarks. Makes sense especially if agents are built on fixing patterns but don't have context of your codebase complexities. Give them a roadmap for the wrong way of doing things so they can learn from it and do better in the future.
Source: x.com/gadi_neelesh/s…
For my newsletter following different topics for start ups including AI, SEO and Social Media Marketing, subscribe to my Feedfree Digest newsletter
*this was posted with FreeSurf's Post, a free social media poster with an API (X api usage paid at cost)
I think every serious coding-agent user should start building a benchmark nobody else can download.
Your own failures.
Not SWE-bench.
Not HumanEval.
Not a leaderboard.
Every time an agent wastes 40 minutes, misunderstands your repo, loops on a tool, edits the wrong file, or rong fix...
save the run.
Because that failure might be worth more than another 1,000 generic benchmark tasks.
Imagine this.
Your agent gets:
Fix checkout occasionally charging users twice.
It spends 25 minutes investigating Stripe.
Wrong direction.
Eventually you discover the actual bug was two workers claiming the same database job.
Today you fix it and move on.
I'd save the entire failed run:
failure-042
TASK
duplicate checkout charge
MODEL
Model A
RESULT
failed
WHERE IT WENT WRONG
step 17
BAD DECISION
focused on Stripe retry behavior
MISSED EVIDENCE
two workers processed the same job ID
TOOLS USED
23
TOKENS
41k
TIME
26 min
Now six weeks later a new model drops.
Everybody asks:
“Is it better for coding?”
You don't need X opinions.
Run it against your graveyard.
Can it solve failure-042?
Then:
failure-019
where the agent edited generated files.
failure-031
where it fixed the symptom instead of the race condition.
failure-055
where it kept increasing a timeout instead of finding shared state.
failure-071
where it changed an API that was supposed to remain compatible.
Now you have a benchmark that actually measures:
Will this model make MY work better?
That's already useful.
But I think you can go much further.
Don't only replay the entire task.
Replay from the exact moment things went wrong.
Example:
STEP 16
Observed:
two identical job IDs in worker logs
STEP 17
Agent chose:
investigate Stripe retries
Freeze everything before step 17.
Now swap only the decision-maker.
Ask 5 models:
What would you do next?
Maybe:
Model A
→ Stripe logs
Model B
→ worker concurrency
Model C
→ database transaction
Model D
→ frontend double-click
Model E
→ worker concurrency
Now you aren't benchmarking who can regenerate an entire app.
You're benchmarking a very specific capability:
who makes the right decision at the exact point my old agent failed?
That is way more interesting.
I'd call these decision checkpoints.
Every painful agent failure should produce one.
Over time you get:
agent-bench/
auth/
expired-session.json
redirect-loop.json
payments/
duplicate-charge.json
webhook-retry.json
database/
stale-read.json
transaction-race.json
testing/
flaky-port.json
shared-state.json
repo-navigation/
wrong-module.json
generated-file.json
Now whenever you change ANYTHING...
new model
new system prompt
new AGENTS.md
new tool
new MCP server
new reasoning level
new agent harness
new memory system
...run the same failure suite again.
And measure:
SOLVED 47 / 60
AVG TOOL CALLS
18 → 11
AVG TOKENS
31k → 19k
WRONG TURNS
14 → 6
REGRESSIONS
3
That last number matters.
Because an agent update can absolutely make some things better and other things worse.
Public benchmarks might say:
+8%
Meanwhile on YOUR repo:
auth debugging improved
but
database migrations got worse.
Without your own replay suite, you'd never know.
This gets even better if you freeze tool outputs.
Imagine the original run called:
Git
database
browser
API
logs
filesystem
Instead of hitting the live world again during evaluation...
record those results.
Then replay them.
Same evidence.
Different model.
Now you're testing the agent's DECISIONS instead of testing whether the outside world happened to behave differently today.
And for every new failure:
add it to the suite.
That's the compounding loop:
AGENT FAILS
↓
SAVE FAILURE
↓
FIND FIRST BAD DECISION
↓
CREATE CHECKPOINT
↓
ADD TO PRIVATE BENCHMARK
↓
TEST FUTURE MODELS / PROMPTS / TOOLS
↓
NEVER ACCEPT THE SAME REGRESSION SILENTLY
This changes how I think about agent failures.
A failed 40-minute session isn't automatically wasted.
It can become a permanent test case for every agent you use in the future.
And imagine doing this for a year.
You might end up with 300 tasks specifically shaped like the problems YOUR codebase produces.
At that point, when a new model launches, you don't need:
“bro is it better than Opus?”
Run:
my-agent-bench --model new-model
and get:
OLD MODEL
213 / 300 solved
$42.10
8h 17m
NEW MODEL
241 / 300 solved
$31.80
6h 04m
IMPROVED
repo navigation
race-condition debugging
frontend fixes
REGRESSED
SQL migrations
dependency upgrades
Now THAT is a model review.
And I'd keep another set:
golden successes.
Runs where the agent did something exceptionally well.
Because you don't only want future systems to avoid old failures.
You also don't want an upgrade to destroy behavior that already worked.
So your benchmark becomes:
FAILURE CORPUS
“Can you avoid mistakes we've already paid for?”
SUCCESS CORPUS
“Can you preserve workflows that already work?”
This also fixes one of the biggest problems with prompt tweaking.
People change AGENTS.md.
Agent feels better.
Maybe.
Change system prompt.
Feels better.
Install a new skill.
Feels better.
Switch model.
Feels WAY better.
Two weeks later something weird breaks.
Instead:
change anything
↓
run private replay suite
↓
compare against baseline
Then you can actually say:
This prompt change:
+9 solved cases
-2 solved cases
18% fewer tool calls
7% more tokens
auth improved
frontend unchanged
database regressed
Now prompt engineering starts looking like engineering.
Not vibes.
And this isn't purely theoretical anymore.
There are open-source tools appearing that record Claude Code / Codex agent runs, replay them step by step, fork execution from specific points, and diff how behavior changes.
Which makes me think the next important developer artifact after:
tests
benchmarks
logs
traces
might be:
agent trajectories.
Because once AI is doing hours of engineering work...
the sequence of decisions that produced the code becomes something worth testing too.
My rule would be:
Every expensive agent failure should become a cheap future regression test.
Don't just fix what the agent got wrong.
Save the moment it got confused.
Then make every future model prove it wouldn't make the same mistake.
Your best coding benchmark probably isn't on the internet.
It's hiding in all the agent runs that pissed you off.
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I made a social media posting software app that's free because it doesn't seem necessary to charge for a posting software when the social media API's are free (my app is free except for X posts at cost).
Consider trying out the web app (API included).
Consider trying out the web app (API included).
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I've been thinking these social media poster apps should be more affordable. So I created a free social media poster with an api (X api fees would be charged at cost; other social platform posting would be free). I should have a mobile app version of this on google play soon.
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A subtle, but significant reason google still outpaces chatgpt:
When I want to search something quickly in my browser, it immediately searches google with the browser query (because it is set to google). Just after typing what I need in the search bar, you get answers from google and its developing ai mode.
With chatgpt, you need to first type in their website, then you can relatively quickly type in your search.
Not a huge difference but a subtle and important one.
Searching in Firefox's browser routes me to Google with what I need.
Chatgpt takes two steps to arrive at what I need.
While chatgpt recently got rid of atlas, I think there is still a significant benefit working directly with browsers to have chatgpt be a search engine since it technically already accomplishes what search engines are doing.
Along with other search engine options like google, duckduckgo, bing or brave, chatgpt could already act as a hub for questions without needing a change in brand.
Right now the only option to integrate chatgpt directly is through extensions (ie someone has to go out of their way to hook it up manually rather than being built into the browser).
And perhaps part of the difficulty is that since google has so much control in the browser industry, chrome can use its influence to effectively make it awkward to integrate.
It's a subtle difference in experience but it is those small pieces of leverage that allow google to sell its own AI products like gemini and its ai mode.
Or perhaps another issue with google's anti-competitive mechanisms.
When I want to search something quickly in my browser, it immediately searches google with the browser query (because it is set to google). Just after typing what I need in the search bar, you get answers from google and its developing ai mode.
With chatgpt, you need to first type in their website, then you can relatively quickly type in your search.
Not a huge difference but a subtle and important one.
Searching in Firefox's browser routes me to Google with what I need.
Chatgpt takes two steps to arrive at what I need.
While chatgpt recently got rid of atlas, I think there is still a significant benefit working directly with browsers to have chatgpt be a search engine since it technically already accomplishes what search engines are doing.
Along with other search engine options like google, duckduckgo, bing or brave, chatgpt could already act as a hub for questions without needing a change in brand.
Right now the only option to integrate chatgpt directly is through extensions (ie someone has to go out of their way to hook it up manually rather than being built into the browser).
And perhaps part of the difficulty is that since google has so much control in the browser industry, chrome can use its influence to effectively make it awkward to integrate.
It's a subtle difference in experience but it is those small pieces of leverage that allow google to sell its own AI products like gemini and its ai mode.
Or perhaps another issue with google's anti-competitive mechanisms.
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AI Engineering 005 - Feedfree Digest
Today you will find several case studies in AI skills in the articles and tweets below.
Instead of large concepts like loop engineering, sometimes we just need to focus on real world use cases applied to specialized fields in the tech ecosystem (though these skills don't need to be limited to tech)
Think:
- How can AI help me or handle my cold outreach?
- How can AI help me create keyword oriented blog posts?
- How can AI help with our finance department?
- How can AI help me create animated characters?
See the articles below to sharpen your specialized situations ->
# Skill Engineering
"The Dark Arts of Skill Engineering"
Author: @pbakaus
Source: x.com/i/web/status/2…
Paul Bakaus, creator of the Impeccable design skill used by hundreds of thousands, shares nine advanced techniques for building agent skills that work consistently across models and harnesses.
- Think of skills as harness extensions, not saved prompts. A great skill makes the model do something it doesn't naturally want to do.
- Use adversarial sub-agents for review: two blind reviewers beat one confident guess. Impeccable's design critique spawns separate agents that never see each other's work.
- Compile per harness and model: Claude Code, Codex, and Cursor have different behaviors. One source SKILL.md compiles to 10+ provider-specific builds.
- PostToolUse hooks fire after every file edit, run deterministic checks, and inject findings as system reminders — guardrails the agent can't skip.
"Agent skill files as the most valuable artifacts on GitHub"
Author: @chewadot
Source: x.com/i/web/status/2…
Well-crafted instruction files that shape agent behavior are now the highest-leverage artifacts, outranking traditional software infrastructure on GitHub.
- If mistakes are predictable, they're preventable. Write the prevention down where the agent reads it — behavior shifts from the first prompt.
- Four principles in one CLAUDE.md: think before coding, simplicity first, surgical changes only, goal-driven execution.
# Outreach & Sales Automation
"How Claude Code + Clay Replaced My Entire SDR Team"
Author: @aiecosystemhq
Source: x.com/i/web/status/2…
A complete workflow replacing a $50K–$80K/year SDR with Claude Code + Clay. 50 enriched leads with verified emails and personalized copy for $12 in credits.
- Create six context files before running (business profile, case studies, FAQs, proof, offer, website copy). Output quality depends entirely on input quality.
- Use /goal with dynamic workflows: Claude Code spawns 6 parallel sub-agents per city, then a verification layer deduplicates and checks every column.
- Every email references a real Google review, recent signal, or specific pain point — not a template.
# SEO with AI help
"I didn't buy an SEO tool. I built one with two markdown files and a scheduled task."
Author: @marcin_knows
Source: x.com/i/web/status/2…
Instead of $99+/month SaaS, this is a complete SEO engine using Claude Max + two markdown files + a weekly rhythm. 4.8x click growth in 3 months.
- Architecture: CONTENT_STRATEGY.md (once), seo-audit.md (weekly), Friday executor that writes, illustrates, builds, and opens a PR.
- The agent found and fixed four broken pages returning HTTP 500 while smoke-testing — no SaaS tool does that.
- ~5 minutes/week of human time: review the PR Saturday morning, approve, merge.
# AI and Finance
"AI for Enterprise Finance & How to Do It Right"
Author: @vasuman
Source: x.com/i/web/status/2…
Real results from Fortune 500 finance AI deployments: one client's close dropped from 12 days to 5, exception handling from 130 hrs/month to 20.
- Skip horizontal assistants (token bloat) and point solutions (don't match your process). Build one agent layer on existing systems.
- Good finance agents are ~85% plain code, ~15% model calls. LLMs only for judgment — reading messy invoices, sorting exceptions.
- Embed with the team first: SOPs rarely capture reality. Watch people work, then build agents that match what they actually do.
# AI Content Creation
"Animating Your Ideas: The Complete AI Anime Playbook (Tutorial + Prompts)"
Author: @apob_ai
Source: x.com/i/web/status/2…
A production-grade AI anime workflow: build the character → create the world → storyboard → edit continuity → animate with direction.
- Don't prompt the whole short at once. Build the character model first, lock identity with a character sheet, then use a 16-panel storyboard before video.
- Structured pre-production beats longer prompts every time.
- Iterate like a director: review character, lighting, camera, emotional beat — fix specific panels in Chat to Edit rather than regenerating everything.
"Mastering AI K-Pop Dance Covers: The Smarter Workflow for Creators (Tutorial + Prompts)"
Author: @Ambani_Wessley
Source: x.com/i/web/status/2…
A structured system for AI dance videos: influencer model → first frame → 16-panel dance storyboard → guided video → Seedance 2.0 output.
- Dance needs identity, rhythm, continuity. Build the influencer model first for a recognizable performer across every clip.
- The 16-panel storyboard is the choreographer — maps body position, outfit, camera angle, and beat timing before animation.
- One influencer model supports TikTok, Reels, Shorts, music teasers, product drops, and UGC ads — all with consistent identity.
If you would like to see more articles like this, please subscribe to my free newsletter in the bio
[disclosure: this was written with a mix of AI and human writing]
Today you will find several case studies in AI skills in the articles and tweets below.
Instead of large concepts like loop engineering, sometimes we just need to focus on real world use cases applied to specialized fields in the tech ecosystem (though these skills don't need to be limited to tech)
Think:
- How can AI help me or handle my cold outreach?
- How can AI help me create keyword oriented blog posts?
- How can AI help with our finance department?
- How can AI help me create animated characters?
See the articles below to sharpen your specialized situations ->
# Skill Engineering
"The Dark Arts of Skill Engineering"
Author: @pbakaus
Source: x.com/i/web/status/2…
Paul Bakaus, creator of the Impeccable design skill used by hundreds of thousands, shares nine advanced techniques for building agent skills that work consistently across models and harnesses.
- Think of skills as harness extensions, not saved prompts. A great skill makes the model do something it doesn't naturally want to do.
- Use adversarial sub-agents for review: two blind reviewers beat one confident guess. Impeccable's design critique spawns separate agents that never see each other's work.
- Compile per harness and model: Claude Code, Codex, and Cursor have different behaviors. One source SKILL.md compiles to 10+ provider-specific builds.
- PostToolUse hooks fire after every file edit, run deterministic checks, and inject findings as system reminders — guardrails the agent can't skip.
"Agent skill files as the most valuable artifacts on GitHub"
Author: @chewadot
Source: x.com/i/web/status/2…
Well-crafted instruction files that shape agent behavior are now the highest-leverage artifacts, outranking traditional software infrastructure on GitHub.
- If mistakes are predictable, they're preventable. Write the prevention down where the agent reads it — behavior shifts from the first prompt.
- Four principles in one CLAUDE.md: think before coding, simplicity first, surgical changes only, goal-driven execution.
# Outreach & Sales Automation
"How Claude Code + Clay Replaced My Entire SDR Team"
Author: @aiecosystemhq
Source: x.com/i/web/status/2…
A complete workflow replacing a $50K–$80K/year SDR with Claude Code + Clay. 50 enriched leads with verified emails and personalized copy for $12 in credits.
- Create six context files before running (business profile, case studies, FAQs, proof, offer, website copy). Output quality depends entirely on input quality.
- Use /goal with dynamic workflows: Claude Code spawns 6 parallel sub-agents per city, then a verification layer deduplicates and checks every column.
- Every email references a real Google review, recent signal, or specific pain point — not a template.
# SEO with AI help
"I didn't buy an SEO tool. I built one with two markdown files and a scheduled task."
Author: @marcin_knows
Source: x.com/i/web/status/2…
Instead of $99+/month SaaS, this is a complete SEO engine using Claude Max + two markdown files + a weekly rhythm. 4.8x click growth in 3 months.
- Architecture: CONTENT_STRATEGY.md (once), seo-audit.md (weekly), Friday executor that writes, illustrates, builds, and opens a PR.
- The agent found and fixed four broken pages returning HTTP 500 while smoke-testing — no SaaS tool does that.
- ~5 minutes/week of human time: review the PR Saturday morning, approve, merge.
# AI and Finance
"AI for Enterprise Finance & How to Do It Right"
Author: @vasuman
Source: x.com/i/web/status/2…
Real results from Fortune 500 finance AI deployments: one client's close dropped from 12 days to 5, exception handling from 130 hrs/month to 20.
- Skip horizontal assistants (token bloat) and point solutions (don't match your process). Build one agent layer on existing systems.
- Good finance agents are ~85% plain code, ~15% model calls. LLMs only for judgment — reading messy invoices, sorting exceptions.
- Embed with the team first: SOPs rarely capture reality. Watch people work, then build agents that match what they actually do.
# AI Content Creation
"Animating Your Ideas: The Complete AI Anime Playbook (Tutorial + Prompts)"
Author: @apob_ai
Source: x.com/i/web/status/2…
A production-grade AI anime workflow: build the character → create the world → storyboard → edit continuity → animate with direction.
- Don't prompt the whole short at once. Build the character model first, lock identity with a character sheet, then use a 16-panel storyboard before video.
- Structured pre-production beats longer prompts every time.
- Iterate like a director: review character, lighting, camera, emotional beat — fix specific panels in Chat to Edit rather than regenerating everything.
"Mastering AI K-Pop Dance Covers: The Smarter Workflow for Creators (Tutorial + Prompts)"
Author: @Ambani_Wessley
Source: x.com/i/web/status/2…
A structured system for AI dance videos: influencer model → first frame → 16-panel dance storyboard → guided video → Seedance 2.0 output.
- Dance needs identity, rhythm, continuity. Build the influencer model first for a recognizable performer across every clip.
- The 16-panel storyboard is the choreographer — maps body position, outfit, camera angle, and beat timing before animation.
- One influencer model supports TikTok, Reels, Shorts, music teasers, product drops, and UGC ads — all with consistent identity.
If you would like to see more articles like this, please subscribe to my free newsletter in the bio
[disclosure: this was written with a mix of AI and human writing]
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Many articles on X cite a common theme for social media success: systems and repetition of what works.
If you find a viral method for presenting your product, keep building on that concept.
For more advice on building systems with social media and other creative how-to's, see these recent X articles below.
->
# Success Case Studies
"How to Make a Highly Successful Mobile App ($0 to $3k in 45 days)"
Author: @frederickjames
Source: x.com/i/web/status/2…
This case study walks through building and monetizing a mobile app from zero to $3,000 in 45 days with no ad budget.
- Launch fast with a minimal but polished product so real user feedback drives your next decisions.
- Use organic social content as your primary distribution channel rather than paid acquisition.
- Track revenue per day, not just total downloads, to know when you have product-market fit.
"The one format that got me 1k MRR in < 1 week"
Author: @jack_mitchell01
Source: x.com/i/web/status/2…
This article isolates a single content format that drove $1,000 in monthly recurring revenue within a week.
- Find one content format that resonates with your audience and run it relentlessly.
- Optimize the hook, the proof point, and the CTA as a unit rather than tweaking them separately.
- Do not overthink the format; consistency of execution matters more than polish.
# Systems with Social Distribution
"The End of Posting. The Beginning of Content Systems."
Author: @somebitcoin
Source: x.com/i/web/status/2…
This article argues that the era of individual viral posts is giving way to systematic content production where process beats inspiration.
- Build a content engine with defined inputs, templates, and publishing cadences.
- Separate ideation from execution so you are never starting from a blank page.
- Measure system throughput, not just post performance, because systems compound while posts decay.
"Stop Working More To Earn More. (How To Earn While You Sleep)"
Author: @soft_continent
Source: x.com/i/web/status/2…
This piece explains how to set up automated revenue streams from content so income continues without constant active effort.
- Build a digital product or affiliate offer that converts from your existing content library.
- Automate distribution and follow-up so your content works as a persistent sales asset.
- Focus on offers that match the intent of your audience rather than chasing every monetization trend.
# How to Build Game Style Characters
"Behind Bernardo: A Rigged Blender Character Workflow"
Author: @_angbury
Source: x.com/i/web/status/2…
This article breaks down the full rigging and character workflow for creating a game-style animated character in Blender.
- Build a reusable rig template so character creation becomes a repeatable production process.
- Document your node setup and export settings so consistency survives across projects.
- Use reference footage or concept art early to lock proportions before investing in detail work.
If you would like to see more articles like this, please subscribe to my free newsletter in the bio
[disclosure: this was written with AI with human edits and article selection.]
If you find a viral method for presenting your product, keep building on that concept.
For more advice on building systems with social media and other creative how-to's, see these recent X articles below.
->
# Success Case Studies
"How to Make a Highly Successful Mobile App ($0 to $3k in 45 days)"
Author: @frederickjames
Source: x.com/i/web/status/2…
This case study walks through building and monetizing a mobile app from zero to $3,000 in 45 days with no ad budget.
- Launch fast with a minimal but polished product so real user feedback drives your next decisions.
- Use organic social content as your primary distribution channel rather than paid acquisition.
- Track revenue per day, not just total downloads, to know when you have product-market fit.
"The one format that got me 1k MRR in < 1 week"
Author: @jack_mitchell01
Source: x.com/i/web/status/2…
This article isolates a single content format that drove $1,000 in monthly recurring revenue within a week.
- Find one content format that resonates with your audience and run it relentlessly.
- Optimize the hook, the proof point, and the CTA as a unit rather than tweaking them separately.
- Do not overthink the format; consistency of execution matters more than polish.
# Systems with Social Distribution
"The End of Posting. The Beginning of Content Systems."
Author: @somebitcoin
Source: x.com/i/web/status/2…
This article argues that the era of individual viral posts is giving way to systematic content production where process beats inspiration.
- Build a content engine with defined inputs, templates, and publishing cadences.
- Separate ideation from execution so you are never starting from a blank page.
- Measure system throughput, not just post performance, because systems compound while posts decay.
"Stop Working More To Earn More. (How To Earn While You Sleep)"
Author: @soft_continent
Source: x.com/i/web/status/2…
This piece explains how to set up automated revenue streams from content so income continues without constant active effort.
- Build a digital product or affiliate offer that converts from your existing content library.
- Automate distribution and follow-up so your content works as a persistent sales asset.
- Focus on offers that match the intent of your audience rather than chasing every monetization trend.
# How to Build Game Style Characters
"Behind Bernardo: A Rigged Blender Character Workflow"
Author: @_angbury
Source: x.com/i/web/status/2…
This article breaks down the full rigging and character workflow for creating a game-style animated character in Blender.
- Build a reusable rig template so character creation becomes a repeatable production process.
- Document your node setup and export settings so consistency survives across projects.
- Use reference footage or concept art early to lock proportions before investing in detail work.
If you would like to see more articles like this, please subscribe to my free newsletter in the bio
[disclosure: this was written with AI with human edits and article selection.]
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Everyday there are new articles explaining key concepts in AI theory on X with many novel use cases and ways of organizing agents for success.
Research on YC backed companies shows a probably obvious trend: most companies receiving funding are finding ways to improve and development upon AI.
Check these X articles below for recent commentary on state of the art use cases and agent design with AI.
->
# Novel Use Case: Claude as an Outbound Team
"turning claude fable 5 into a full outbound team (6 plays, every prompt included)"
Author: @DinScales26
Source: x.com/i/web/status/2…
This article shows how Fable 5 can be retooled from a coding assistant into a complete outbound sales operation with six repeatable playbooks.
- Build play-specific prompts that include ICP criteria, research steps, and deliverable formats.
- Run parallel agent lanes for list building, enrichment, and messaging so each stage gets focused attention.
- Measure pipeline generated per prompt iteration so you can improve the system week over week.
# Scope With Agent Work
"One question for building your next AI agent (JTBD)"
Author: @caelanhuntress
Source: x.com/i/web/status/2…
This article applies Jobs-to-be-Done thinking to agent design, arguing that the most important question is what job the agent is hired to do.
- Define the agent's job in terms of an outcome the user wants, not a feature list.
- Use the JTBD frame to decide what the agent should NOT do, which is often more important than what it can do.
- Test the agent against real user progress, not just task completion metrics.
# Most Recent YC Backed Companies Are AI Oriented
"I analysed every YC backed company from the past 24 months"
Author: @AdeimantosAI
Source: x.com/i/web/status/2…
This analysis of recent Y Combinator cohorts shows AI has become the dominant category, reshaping what investors and founders are betting on.
- Expect AI-native startups to define competitive benchmarks in every vertical.
- Look for AI as infrastructure rather than as a standalone product category.
- Track cohort data as a leading indicator of where engineering talent and capital are flowing.
# Model Testing
"The model didn't matter. Then it did."
Author: @0xBakeer
Source: x.com/i/web/status/2…
This article ran four models through progressively harder tests and found that model choice only matters past a certain complexity threshold.
- Run your own benchmarks on your own tasks before committing to a model for production.
- Use simple tasks to validate your harness and complex tasks to differentiate models.
- Treat model selection as a moving target that depends on task difficulty, not brand reputation.
# Loop Engineering Advice
"10 Real-World Loop Engineering Examples in 2026"
Author: @vicky_grok
Source: x.com/i/web/status/2…
This piece catalogs ten production loop engineering examples across content, bug fixing, research, learning, support, sales, intelligence, finance, social media, and legal use cases.
- Use the Map → Learn → Test → Compress → Repeat cycle as a starting template for any new loop.
- Apply loops to high-volume repetitive work first because the ROI compounds fastest there.
- Keep a record of failed loop iterations because they teach you what the system cannot handle yet.
# Memory / Context Storing
"we just built an infinite context window for ai"
Author: @daleverett
Source: x.com/i/web/status/2…
This article describes a system that gives AI effectively unlimited context by structuring memory as a searchable external layer rather than cramming everything into a single prompt.
- Move long-term memory outside the context window and treat it as a retrieval problem.
- Index knowledge by task and recency so the AI pulls in what matters without being overwhelmed.
- Test retrieval quality separately from generation quality when diagnosing memory failures.
"Context rot: the study that proves your million-token context window is lying to you"
Author: @0xCarnagee
Source: x.com/i/web/status/2…
This study demonstrates that larger context windows degrade in reliability, with models losing accuracy on information in the middle and later portions of long inputs.
- Do not assume a 1M token window means the model can reason over 1M tokens effectively.
- Structure critical information near the beginning of the context where attention is strongest.
- Test your prompts for positional degradation before relying on long-context reasoning in production.
"Enterprise GraphRAG: Graph + Vector + Agents (Open Source Stack)"
Author: @AkhileshASM
Source: x.com/i/web/status/2…
This article presents an open-source stack that combines graph databases, vector search, and agents to build enterprise-grade RAG systems.
- Use graph relationships to represent connections that vector similarity alone would miss.
- Combine retrieval methods so structured and unstructured data are queried through one interface.
- Plan for agent orchestration early because the retrieval layer is only as useful as the reasoning layer above it.
# AI Safety
"AI Is Becoming Unknowable - The Cost of Intelligence"
Author: @AiRaForSupra
Source: x.com/i/web/status/2…
This article argues that as models get more capable, they also become less interpretable, and that uninterpretability itself is a growing risk.
- Build evaluation pipelines that test for behaviors you cannot inspect directly.
- Treat interpretability as a first-class requirement in agent design, not a research footnote.
- Plan for the operational cost of not understanding why a model made a decision.
"The AI Governance Illusion: Benchmarks Are Not Mandates"
Author: @mantancino_
Source: x.com/i/web/status/2…
This piece argues that passing AI benchmarks does not equal being governed, and that the gap between measurement and mandate is where real risk lives.
- Distinguish between passing a test and being safe to deploy in an uncontrolled environment.
- Build governance processes that go beyond benchmark scores and include operational safeguards.
- Treat benchmarks as a floor for capability, not a ceiling for responsibility.
If you would like to see more articles like this, please subscribe to my free newsletter in the bio
[disclosure: this was written with AI with human edits and article selection.]
Research on YC backed companies shows a probably obvious trend: most companies receiving funding are finding ways to improve and development upon AI.
Check these X articles below for recent commentary on state of the art use cases and agent design with AI.
->
# Novel Use Case: Claude as an Outbound Team
"turning claude fable 5 into a full outbound team (6 plays, every prompt included)"
Author: @DinScales26
Source: x.com/i/web/status/2…
This article shows how Fable 5 can be retooled from a coding assistant into a complete outbound sales operation with six repeatable playbooks.
- Build play-specific prompts that include ICP criteria, research steps, and deliverable formats.
- Run parallel agent lanes for list building, enrichment, and messaging so each stage gets focused attention.
- Measure pipeline generated per prompt iteration so you can improve the system week over week.
# Scope With Agent Work
"One question for building your next AI agent (JTBD)"
Author: @caelanhuntress
Source: x.com/i/web/status/2…
This article applies Jobs-to-be-Done thinking to agent design, arguing that the most important question is what job the agent is hired to do.
- Define the agent's job in terms of an outcome the user wants, not a feature list.
- Use the JTBD frame to decide what the agent should NOT do, which is often more important than what it can do.
- Test the agent against real user progress, not just task completion metrics.
# Most Recent YC Backed Companies Are AI Oriented
"I analysed every YC backed company from the past 24 months"
Author: @AdeimantosAI
Source: x.com/i/web/status/2…
This analysis of recent Y Combinator cohorts shows AI has become the dominant category, reshaping what investors and founders are betting on.
- Expect AI-native startups to define competitive benchmarks in every vertical.
- Look for AI as infrastructure rather than as a standalone product category.
- Track cohort data as a leading indicator of where engineering talent and capital are flowing.
# Model Testing
"The model didn't matter. Then it did."
Author: @0xBakeer
Source: x.com/i/web/status/2…
This article ran four models through progressively harder tests and found that model choice only matters past a certain complexity threshold.
- Run your own benchmarks on your own tasks before committing to a model for production.
- Use simple tasks to validate your harness and complex tasks to differentiate models.
- Treat model selection as a moving target that depends on task difficulty, not brand reputation.
# Loop Engineering Advice
"10 Real-World Loop Engineering Examples in 2026"
Author: @vicky_grok
Source: x.com/i/web/status/2…
This piece catalogs ten production loop engineering examples across content, bug fixing, research, learning, support, sales, intelligence, finance, social media, and legal use cases.
- Use the Map → Learn → Test → Compress → Repeat cycle as a starting template for any new loop.
- Apply loops to high-volume repetitive work first because the ROI compounds fastest there.
- Keep a record of failed loop iterations because they teach you what the system cannot handle yet.
# Memory / Context Storing
"we just built an infinite context window for ai"
Author: @daleverett
Source: x.com/i/web/status/2…
This article describes a system that gives AI effectively unlimited context by structuring memory as a searchable external layer rather than cramming everything into a single prompt.
- Move long-term memory outside the context window and treat it as a retrieval problem.
- Index knowledge by task and recency so the AI pulls in what matters without being overwhelmed.
- Test retrieval quality separately from generation quality when diagnosing memory failures.
"Context rot: the study that proves your million-token context window is lying to you"
Author: @0xCarnagee
Source: x.com/i/web/status/2…
This study demonstrates that larger context windows degrade in reliability, with models losing accuracy on information in the middle and later portions of long inputs.
- Do not assume a 1M token window means the model can reason over 1M tokens effectively.
- Structure critical information near the beginning of the context where attention is strongest.
- Test your prompts for positional degradation before relying on long-context reasoning in production.
"Enterprise GraphRAG: Graph + Vector + Agents (Open Source Stack)"
Author: @AkhileshASM
Source: x.com/i/web/status/2…
This article presents an open-source stack that combines graph databases, vector search, and agents to build enterprise-grade RAG systems.
- Use graph relationships to represent connections that vector similarity alone would miss.
- Combine retrieval methods so structured and unstructured data are queried through one interface.
- Plan for agent orchestration early because the retrieval layer is only as useful as the reasoning layer above it.
# AI Safety
"AI Is Becoming Unknowable - The Cost of Intelligence"
Author: @AiRaForSupra
Source: x.com/i/web/status/2…
This article argues that as models get more capable, they also become less interpretable, and that uninterpretability itself is a growing risk.
- Build evaluation pipelines that test for behaviors you cannot inspect directly.
- Treat interpretability as a first-class requirement in agent design, not a research footnote.
- Plan for the operational cost of not understanding why a model made a decision.
"The AI Governance Illusion: Benchmarks Are Not Mandates"
Author: @mantancino_
Source: x.com/i/web/status/2…
This piece argues that passing AI benchmarks does not equal being governed, and that the gap between measurement and mandate is where real risk lives.
- Distinguish between passing a test and being safe to deploy in an uncontrolled environment.
- Build governance processes that go beyond benchmark scores and include operational safeguards.
- Treat benchmarks as a floor for capability, not a ceiling for responsibility.
If you would like to see more articles like this, please subscribe to my free newsletter in the bio
[disclosure: this was written with AI with human edits and article selection.]
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Growing on X, tiktok or other channels can be a serious challenge for most of us (me included). However below you will see articles written by those with significant success with social media to drive their products or services.
For instance a push-up app designed to get people to stop doomscrolling just cleared 1 million ARR.
Everyday people are creating apps and monetizing channels in ways it would have never been possible several years ago.
Take a look at the summaries below and check out their respective articles if you want to learn more
->
# TikTok Growth
"How I marketed my Screen-Time app to $1,000,000 (with a $0 budget)"
Author: @skyirezumi
Source: x.com/i/web/status/2…
This case study breaks down how a founder drove serious revenue through organic TikTok without paid spend.
- Build content around the problem your product solves rather than around the product itself.
- Post consistently enough that the algorithm has enough signal to learn who to show your videos to.
- Turn comments and duets into a feedback loop that shapes the next batch of content.
"The only 2 content formats you need to hit 100K TikTok views for your app:"
Author: @eglitisX
Source: x.com/i/web/status/2…
This article simplifies TikTok growth for app founders down to two repeatable formats that reliably attract views.
- Identify the two formats your audience already responds to and master them before expanding.
- Optimize the first one to three seconds so the hook stops the scroll.
- Reuse winning formats with new angles rather than reinventing the structure each time.
"The ULTIMATE TikTok Organic Marketing Guide for App Founders..."
Author: @karilovv
Source: x.com/i/web/status/2…
This guide pulls together the full organic playbook for app founders trying to grow on TikTok.
- Treat TikTok as a discovery channel where proof and personality matter more than polished production.
- Design a posting cadence you can sustain while still responding to comments and trends.
- Track the right proxy metrics, because virality without downloads is not real growth.
# AI Video Generation
"The actual pipeline behind my AI videos"
Author: @adam_rosler
Source: x.com/i/web/status/2…
This article reveals the real workflow behind consistent AI-generated video output, not just the finished clips.
- Document every stage from idea to final export so the process can be repeated and improved.
- Separate generation, editing, and quality control so one weak step does not ruin the whole asset.
- Build a prompt and parameter library so the style stays consistent across posts.
"Claude's Fable 5 Is CRACKED for ORGANIC AI UGC..."
Author: @adriansolarzz
Source: x.com/i/web/status/2…
This piece explains how Fable 5 can be used to produce user-generated-style content that performs organically.
- Use AI to lower the cost of testing many UGC angles quickly.
- Keep the scripts grounded in real user language rather than generic marketing speak.
- Review every output for authenticity because audiences are quick to dismiss synthetic-feeling UGC.
"AI Anime Creation Playbook: Turning Story Ideas Into a Complete Production Workflow (Tutorial & Prompt)"
Author: @apob_ai
Source: x.com/i/web/status/2…
This playbook maps out how to turn a story idea into a finished AI anime production through a structured workflow.
- Break the project into story, visual style, generation, and post-production stages.
- Lock the style guide early so every scene feels like it belongs to the same world.
- Save successful prompts and settings as reusable production assets.
# X Growth
"Viral trends on X"
Author: @James_apolo101
Source: x.com/i/web/status/2…
This article tracks what is currently working on X and how to ride trends without looking like a copycat.
- Move fast on trends while they are still fresh, but add your own point of view.
- Study the structure of viral posts rather than just the topic.
- Use trends as entry points to your own themes so the audience has a reason to stay.
"Mastering the X Algorithm: The Blueprint to Build Authority and Income"
Author: @GetMrVic
Source: x.com/i/web/status/2…
This piece frames the X algorithm as a system that rewards consistency, engagement depth, and clear positioning.
- Post around a narrow set of topics so the algorithm learns who to surface your content to.
- Engage genuinely in replies because reciprocal interaction signals matter.
- Convert attention into authority by backing claims with proof and stories.
# Ad Structure
"How to Start TikTok Ads With Completely Untested Creatives (Print $$$ in Weeks)"
Author: @maxzrco
Source: x.com/i/web/status/2…
This article explains how to launch TikTok ads quickly even when you do not have proven creative assets yet.
- Launch with volume and let early spend data tell you which angles survive.
- Build variations around a few core hooks rather than one polished hero video.
- Kill losers fast and double down on the first creative that shows positive signs.
"How To Find Unlimited New Ad Angles"
Author: @jakobcounts123
Source: x.com/i/web/status/2…
This piece provides a system for generating fresh ad angles so campaigns do not stagnate.
- Mine reviews, support tickets, and sales calls for the exact language customers use.
- Rotate angles around different emotions, outcomes, and objections.
- Keep a running library of proven hooks so new creative starts from a strong base.
# YouTube Growth
"📘 The Creator Blueprint #4 How the YouTube Algorithm Really Works (2026 Edition):"
Author: @Themrbachelor
Source: x.com/i/web/status/2…
This article updates the YouTube algorithm playbook for the current environment with a focus on practical signals.
- Prioritize click-through rate and watch time by matching thumbnail and title to delivery.
- Build series and playlists that keep viewers on your channel longer.
- Use audience retention graphs to identify exactly where attention drops.
[disclosure: this was written with AI with human edits and article selection. If you would like to see more articles like this, please subscribe to my free newsletter in the bio]
For instance a push-up app designed to get people to stop doomscrolling just cleared 1 million ARR.
Everyday people are creating apps and monetizing channels in ways it would have never been possible several years ago.
Take a look at the summaries below and check out their respective articles if you want to learn more
->
# TikTok Growth
"How I marketed my Screen-Time app to $1,000,000 (with a $0 budget)"
Author: @skyirezumi
Source: x.com/i/web/status/2…
This case study breaks down how a founder drove serious revenue through organic TikTok without paid spend.
- Build content around the problem your product solves rather than around the product itself.
- Post consistently enough that the algorithm has enough signal to learn who to show your videos to.
- Turn comments and duets into a feedback loop that shapes the next batch of content.
"The only 2 content formats you need to hit 100K TikTok views for your app:"
Author: @eglitisX
Source: x.com/i/web/status/2…
This article simplifies TikTok growth for app founders down to two repeatable formats that reliably attract views.
- Identify the two formats your audience already responds to and master them before expanding.
- Optimize the first one to three seconds so the hook stops the scroll.
- Reuse winning formats with new angles rather than reinventing the structure each time.
"The ULTIMATE TikTok Organic Marketing Guide for App Founders..."
Author: @karilovv
Source: x.com/i/web/status/2…
This guide pulls together the full organic playbook for app founders trying to grow on TikTok.
- Treat TikTok as a discovery channel where proof and personality matter more than polished production.
- Design a posting cadence you can sustain while still responding to comments and trends.
- Track the right proxy metrics, because virality without downloads is not real growth.
# AI Video Generation
"The actual pipeline behind my AI videos"
Author: @adam_rosler
Source: x.com/i/web/status/2…
This article reveals the real workflow behind consistent AI-generated video output, not just the finished clips.
- Document every stage from idea to final export so the process can be repeated and improved.
- Separate generation, editing, and quality control so one weak step does not ruin the whole asset.
- Build a prompt and parameter library so the style stays consistent across posts.
"Claude's Fable 5 Is CRACKED for ORGANIC AI UGC..."
Author: @adriansolarzz
Source: x.com/i/web/status/2…
This piece explains how Fable 5 can be used to produce user-generated-style content that performs organically.
- Use AI to lower the cost of testing many UGC angles quickly.
- Keep the scripts grounded in real user language rather than generic marketing speak.
- Review every output for authenticity because audiences are quick to dismiss synthetic-feeling UGC.
"AI Anime Creation Playbook: Turning Story Ideas Into a Complete Production Workflow (Tutorial & Prompt)"
Author: @apob_ai
Source: x.com/i/web/status/2…
This playbook maps out how to turn a story idea into a finished AI anime production through a structured workflow.
- Break the project into story, visual style, generation, and post-production stages.
- Lock the style guide early so every scene feels like it belongs to the same world.
- Save successful prompts and settings as reusable production assets.
# X Growth
"Viral trends on X"
Author: @James_apolo101
Source: x.com/i/web/status/2…
This article tracks what is currently working on X and how to ride trends without looking like a copycat.
- Move fast on trends while they are still fresh, but add your own point of view.
- Study the structure of viral posts rather than just the topic.
- Use trends as entry points to your own themes so the audience has a reason to stay.
"Mastering the X Algorithm: The Blueprint to Build Authority and Income"
Author: @GetMrVic
Source: x.com/i/web/status/2…
This piece frames the X algorithm as a system that rewards consistency, engagement depth, and clear positioning.
- Post around a narrow set of topics so the algorithm learns who to surface your content to.
- Engage genuinely in replies because reciprocal interaction signals matter.
- Convert attention into authority by backing claims with proof and stories.
# Ad Structure
"How to Start TikTok Ads With Completely Untested Creatives (Print $$$ in Weeks)"
Author: @maxzrco
Source: x.com/i/web/status/2…
This article explains how to launch TikTok ads quickly even when you do not have proven creative assets yet.
- Launch with volume and let early spend data tell you which angles survive.
- Build variations around a few core hooks rather than one polished hero video.
- Kill losers fast and double down on the first creative that shows positive signs.
"How To Find Unlimited New Ad Angles"
Author: @jakobcounts123
Source: x.com/i/web/status/2…
This piece provides a system for generating fresh ad angles so campaigns do not stagnate.
- Mine reviews, support tickets, and sales calls for the exact language customers use.
- Rotate angles around different emotions, outcomes, and objections.
- Keep a running library of proven hooks so new creative starts from a strong base.
# YouTube Growth
"📘 The Creator Blueprint #4 How the YouTube Algorithm Really Works (2026 Edition):"
Author: @Themrbachelor
Source: x.com/i/web/status/2…
This article updates the YouTube algorithm playbook for the current environment with a focus on practical signals.
- Prioritize click-through rate and watch time by matching thumbnail and title to delivery.
- Build series and playlists that keep viewers on your channel longer.
- Use audience retention graphs to identify exactly where attention drops.
[disclosure: this was written with AI with human edits and article selection. If you would like to see more articles like this, please subscribe to my free newsletter in the bio]
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Search visibility is now a hybrid of:
- classic SEO,
- AI overview optimization
- agent-assisted execution
the winners are treating all three as parts of one system.
See below for summaries of some of the most helpful articles on X in the SEO space recently.
# Landing in AI Overviews
"Rank #1 on AI Search with Instagram Carousels (Automated with Claude)"
Author: @borjafat
Source: x.com/i/web/status/2…
This article shows how Instagram carousels can be engineered to show up as the source in AI-generated search answers.
- Format content as structured, visual answers that AI search engines can easily cite.
- Use Claude to speed up carousel production without losing the human editorial layer.
- Optimize for the questions people actually ask rather than chasing head keywords alone.
"I Reverse Engineered 1000s of AI Overviews: Here's Exactly How To Get Google To Cite YOU"
Author: @Charles_SEO
Source: x.com/i/web/status/2…
This piece distills patterns from thousands of AI overviews into a repeatable method for earning citations.
- Give direct, well-structured answers early in the content so AI systems can extract them cleanly.
- Use clear headings, lists, and definitions that match the format AI overviews prefer.
- Build topical authority because citations tend to cluster around trusted sources.
"How To Trick Chatbots into Recommending Your Product"
Author: @Jlafortetech
Source: x.com/i/web/status/2…
This article explores how to influence the recommendation behavior of AI assistants and chatbots in your favor.
- Understand how chatbots synthesize answers from training data, crawled content, and context.
- Create content that clearly associates your product with the problems it solves.
- Avoid manipulative framing that could backfire as models and platforms improve detection.
"How I grew my traffic by 75% with Ranking on AI Answers"
Author: @withhimu
Source: x.com/i/web/status/2…
This case study explains how one site captured significant traffic by optimizing for AI answer engines instead of traditional rankings alone.
- Rewrite key pages to answer specific questions in a format AI can quote.
- Focus on long-tail informational queries where AI answers are most likely to appear.
- Measure traffic from AI referrals separately so you can iterate on what works.
"How to Use Reddit for SEO and AI Visibility: A SEORCE Guide"
Author: @seorce_
Source: x.com/i/web/status/2…
This guide explains how Reddit fits into modern SEO and AI visibility strategies.
- Find the subreddits where your audience already asks the questions you answer best.
- Participate authentically because both users and AI systems reward genuine expertise.
- Use Reddit discussions as a source of content ideas and proof points for your own pages.
"SEORCE Guide: 6 SEO Priorities to Rethink for AI Search"
Author: @seorce_
Source: x.com/i/web/status/2…
This article identifies six SEO priorities that need to change now that AI is reshaping how search results are built.
- Shift some effort from ranking position to being cited inside AI-generated summaries.
- Invest in content clarity and structure because extraction quality depends on it.
- Re-evaluate which keywords still justify traditional optimization versus AI-first formats.
"SEO Roadmap Strategy For Ranking On Google And AI Search"
Author: @JulianGoldieSEO
Source: x.com/i/web/status/2…
This piece lays out a practical roadmap for ranking in both classic Google results and the newer AI search experiences.
- Build a foundation of technical health and internal linking before chasing AI-specific tactics.
- Create content that serves both human readers and machine extraction.
- Track rankings, citations, and referral traffic together to see the full picture.
"Most advice about "AI SEO" skips the most important part."
Author: @_asifsyed
Source: x.com/i/web/status/2…
This article argues that most AI SEO conversations ignore the fundamentals that still make everything else possible.
- Get the basics right first: crawlability, site speed, clear content, and internal links.
- Treat AI search as an extension of good SEO, not a replacement for it.
- Focus on satisfying user intent because that remains the signal both engines optimize for.
# Agent SEO Work
"Claude Fable 5, my New pSEO Engine"
Author: @borjafat
Source: x.com/i/web/status/2…
This article explains how Fable 5 is being used as a programmable SEO engine to scale programmatic content.
- Use agents to generate structured content at scale while preserving editorial standards.
- Build templates and data pipelines so the output is consistent and maintainable.
- Review agent output for accuracy and originality before publishing.
"25 AI Agent Loops That Turn Fable 5 Into an Autonomous Business Operator"
Author: @DivyanshT91162
Source: x.com/i/web/status/2…
This piece catalogs loops that let Fable 5 run parts of a business with minimal human intervention.
- Identify repeatable SEO tasks that can be turned into agent loops, such as briefs, drafts, and updates.
- Add checkpoints so a human can approve or correct work before it goes live.
- Start with one loop and measure outcomes before wiring everything together.
"I rebuilt FooPlugins.com in three weeks with an agent dev team"
Author: @bradvin
Source: x.com/i/web/status/2…
This case study shows how agent-assisted development can compress a major site rebuild into weeks.
- Break the project into modules that agents can own while humans handle architecture and review.
- Use agents for code generation, testing, and refactoring to move faster.
- Keep quality gates strict because speed is only valuable if the result is reliable.
"New Qwen Model Runs Local SEO Workflows FREE"
Author: @JulianGoldieSEO
Source: x.com/i/web/status/2…
This article introduces a local model option that can run SEO workflows without API costs.
- Test local models for SEO tasks that do not require frontier-level reasoning.
- Compare output quality and latency against cloud models before switching workflows.
- Factor in hardware, setup, and maintenance when calculating true cost savings.
# Social Media SEO
"Pinterest SEO 101: How It Really Works in 2026 (and What Changes with AI)"
Author: @seocopydebby
Source: x.com/i/web/status/2…
This article explains how Pinterest search works today and where AI is changing the rules.
- Optimize pins for visual search and keyword intent, not just aesthetics.
- Refresh content regularly because Pinterest favors active accounts and recent saves.
- Use AI to speed up pin creation while keeping each pin aligned to a specific search query.
[disclosure: this was written with AI with human edits and article selection. If you would like to see more articles like this, please subscribe to my free newsletter in the bio]
- classic SEO,
- AI overview optimization
- agent-assisted execution
the winners are treating all three as parts of one system.
See below for summaries of some of the most helpful articles on X in the SEO space recently.
# Landing in AI Overviews
"Rank #1 on AI Search with Instagram Carousels (Automated with Claude)"
Author: @borjafat
Source: x.com/i/web/status/2…
This article shows how Instagram carousels can be engineered to show up as the source in AI-generated search answers.
- Format content as structured, visual answers that AI search engines can easily cite.
- Use Claude to speed up carousel production without losing the human editorial layer.
- Optimize for the questions people actually ask rather than chasing head keywords alone.
"I Reverse Engineered 1000s of AI Overviews: Here's Exactly How To Get Google To Cite YOU"
Author: @Charles_SEO
Source: x.com/i/web/status/2…
This piece distills patterns from thousands of AI overviews into a repeatable method for earning citations.
- Give direct, well-structured answers early in the content so AI systems can extract them cleanly.
- Use clear headings, lists, and definitions that match the format AI overviews prefer.
- Build topical authority because citations tend to cluster around trusted sources.
"How To Trick Chatbots into Recommending Your Product"
Author: @Jlafortetech
Source: x.com/i/web/status/2…
This article explores how to influence the recommendation behavior of AI assistants and chatbots in your favor.
- Understand how chatbots synthesize answers from training data, crawled content, and context.
- Create content that clearly associates your product with the problems it solves.
- Avoid manipulative framing that could backfire as models and platforms improve detection.
"How I grew my traffic by 75% with Ranking on AI Answers"
Author: @withhimu
Source: x.com/i/web/status/2…
This case study explains how one site captured significant traffic by optimizing for AI answer engines instead of traditional rankings alone.
- Rewrite key pages to answer specific questions in a format AI can quote.
- Focus on long-tail informational queries where AI answers are most likely to appear.
- Measure traffic from AI referrals separately so you can iterate on what works.
"How to Use Reddit for SEO and AI Visibility: A SEORCE Guide"
Author: @seorce_
Source: x.com/i/web/status/2…
This guide explains how Reddit fits into modern SEO and AI visibility strategies.
- Find the subreddits where your audience already asks the questions you answer best.
- Participate authentically because both users and AI systems reward genuine expertise.
- Use Reddit discussions as a source of content ideas and proof points for your own pages.
"SEORCE Guide: 6 SEO Priorities to Rethink for AI Search"
Author: @seorce_
Source: x.com/i/web/status/2…
This article identifies six SEO priorities that need to change now that AI is reshaping how search results are built.
- Shift some effort from ranking position to being cited inside AI-generated summaries.
- Invest in content clarity and structure because extraction quality depends on it.
- Re-evaluate which keywords still justify traditional optimization versus AI-first formats.
"SEO Roadmap Strategy For Ranking On Google And AI Search"
Author: @JulianGoldieSEO
Source: x.com/i/web/status/2…
This piece lays out a practical roadmap for ranking in both classic Google results and the newer AI search experiences.
- Build a foundation of technical health and internal linking before chasing AI-specific tactics.
- Create content that serves both human readers and machine extraction.
- Track rankings, citations, and referral traffic together to see the full picture.
"Most advice about "AI SEO" skips the most important part."
Author: @_asifsyed
Source: x.com/i/web/status/2…
This article argues that most AI SEO conversations ignore the fundamentals that still make everything else possible.
- Get the basics right first: crawlability, site speed, clear content, and internal links.
- Treat AI search as an extension of good SEO, not a replacement for it.
- Focus on satisfying user intent because that remains the signal both engines optimize for.
# Agent SEO Work
"Claude Fable 5, my New pSEO Engine"
Author: @borjafat
Source: x.com/i/web/status/2…
This article explains how Fable 5 is being used as a programmable SEO engine to scale programmatic content.
- Use agents to generate structured content at scale while preserving editorial standards.
- Build templates and data pipelines so the output is consistent and maintainable.
- Review agent output for accuracy and originality before publishing.
"25 AI Agent Loops That Turn Fable 5 Into an Autonomous Business Operator"
Author: @DivyanshT91162
Source: x.com/i/web/status/2…
This piece catalogs loops that let Fable 5 run parts of a business with minimal human intervention.
- Identify repeatable SEO tasks that can be turned into agent loops, such as briefs, drafts, and updates.
- Add checkpoints so a human can approve or correct work before it goes live.
- Start with one loop and measure outcomes before wiring everything together.
"I rebuilt FooPlugins.com in three weeks with an agent dev team"
Author: @bradvin
Source: x.com/i/web/status/2…
This case study shows how agent-assisted development can compress a major site rebuild into weeks.
- Break the project into modules that agents can own while humans handle architecture and review.
- Use agents for code generation, testing, and refactoring to move faster.
- Keep quality gates strict because speed is only valuable if the result is reliable.
"New Qwen Model Runs Local SEO Workflows FREE"
Author: @JulianGoldieSEO
Source: x.com/i/web/status/2…
This article introduces a local model option that can run SEO workflows without API costs.
- Test local models for SEO tasks that do not require frontier-level reasoning.
- Compare output quality and latency against cloud models before switching workflows.
- Factor in hardware, setup, and maintenance when calculating true cost savings.
# Social Media SEO
"Pinterest SEO 101: How It Really Works in 2026 (and What Changes with AI)"
Author: @seocopydebby
Source: x.com/i/web/status/2…
This article explains how Pinterest search works today and where AI is changing the rules.
- Optimize pins for visual search and keyword intent, not just aesthetics.
- Refresh content regularly because Pinterest favors active accounts and recent saves.
- Use AI to speed up pin creation while keeping each pin aligned to a specific search query.
[disclosure: this was written with AI with human edits and article selection. If you would like to see more articles like this, please subscribe to my free newsletter in the bio]
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Several major authorities in the AI space are recommending moving towards loop engineering.
It makes sense as realistically what is holding AI back most?
The human in the loop.
Consider reading these recent X articles below for staying present with some of the best wisdom in the AI space.
# Loop Engineering
"How To Build Your First AI Loop in 2026"
Author: @sairahul1
Source: x.com/i/web/status/2…
This article makes loop building concrete for practitioners who are ready to move beyond one-shot prompts.
- Define a clear stop condition before the loop starts so it does not spin indefinitely.
- Give the loop a way to measure progress against the goal on each iteration.
- Keep the first version simple and observable before adding self-healing or multi-agent steps.
"The End of Prompting: The Beginning of AI Loops"
Author: @Nekt_0
Source: x.com/i/web/status/2…
This piece argues that loops replace static prompting because they let the system iterate, evaluate, and decide what to do next.
- Stop trying to encode every edge case in a single prompt and instead build a repeatable cycle.
- Add an evaluation step that can accept, reject, or request revisions.
- Design loops that can hand off to a human cleanly when they hit uncertainty.
# Harness Engineering
"Cost effective harnesses with Fable"
Author: @RLanceMartin
Source: x.com/i/web/status/2…
This article shows how to keep agent harnesses cheap without sacrificing reliability by being deliberate about what gets delegated to the model.
- Design harnesses around minimum viable model calls rather than letting the agent run open-ended.
- Use cheaper models or shorter contexts for validation, routing, and formatting while reserving heavy models for real reasoning.
- Measure cost per successful outcome so optimization targets the full system, not just the prompt.
# Prompt Engineering
"GPT-5.6 Prompting Masterclass (by OpenAI)"
Author: @aiedge_
Source: x.com/i/web/status/2…
This masterclass distills the current OpenAI guidance on prompting into practical patterns that improve model output quality and consistency.
- Be explicit about the role, task format, and success criteria before asking for the answer.
- Use structured delimiters and examples to reduce ambiguity in complex requests.
- Iterate on instructions based on failure modes rather than rewriting prompts from scratch every time.
# Second Brain / Memory Engineering
"Build a memory system that survives across sessions with Fable 5: write, consolidate, recall, apply"
Author: @cyrilXBT
Source: x.com/i/web/status/2…
This article treats memory as an active loop where information is captured, refined, retrieved, and put to use instead of simply stored.
- Separate capture from consolidation so raw notes do not clutter the retrieval layer.
- Build recall around the task at hand rather than returning the entire memory history.
- Close the loop by applying retrieved memory to a real action and feeding the result back in.
"I built a living 24x7 Knowledge Graph in Obsidian that updates itself daily with AI"
Author: @KanikaBK
Source: x.com/i/web/status/2…
This piece demonstrates how a knowledge graph can become a self-updating thinking tool when paired with a daily AI ingestion habit.
- Structure notes as connected entities and relationships rather than isolated documents.
- Automate a small daily review pipeline that identifies new links and updates stale nodes.
- Treat the graph as a reasoning surface for the agent, not just a static reference.
"How to Build an AI Memory System in 30 Minutes (Templates Included)"
Author: @ivanainai
Source: x.com/i/web/status/2…
This piece makes long-term memory approachable by breaking it into a quick-build system with ready-to-use templates.
- Start with a small, structured memory schema instead of dumping every conversation into storage.
- Use templates for read, write, and summarize operations so memory stays consistent across sessions.
- Test memory retrieval against real tasks before scaling the system to more data.
# Jarvis-like Agent Engineering
"Hermes Agent Master Class Part 5: Tools and Toolsets"
Author: @tonysimons
Source: x.com/i/web/status/2…
This installment focuses on how tools and toolsets turn a language model into an agent that can act on the world.
- Group tools into coherent toolsets so the agent chooses from a curated menu rather than a chaotic list.
- Describe each tool in terms the model can reason about, including when not to use it.
- Test tool calls in isolation before wiring them into autonomous loops.
[disclosure: this was written with AI with human edits and article selection. If you would like to see more articles like this, please subscribe to my free newsletter in the bio]
It makes sense as realistically what is holding AI back most?
The human in the loop.
Consider reading these recent X articles below for staying present with some of the best wisdom in the AI space.
# Loop Engineering
"How To Build Your First AI Loop in 2026"
Author: @sairahul1
Source: x.com/i/web/status/2…
This article makes loop building concrete for practitioners who are ready to move beyond one-shot prompts.
- Define a clear stop condition before the loop starts so it does not spin indefinitely.
- Give the loop a way to measure progress against the goal on each iteration.
- Keep the first version simple and observable before adding self-healing or multi-agent steps.
"The End of Prompting: The Beginning of AI Loops"
Author: @Nekt_0
Source: x.com/i/web/status/2…
This piece argues that loops replace static prompting because they let the system iterate, evaluate, and decide what to do next.
- Stop trying to encode every edge case in a single prompt and instead build a repeatable cycle.
- Add an evaluation step that can accept, reject, or request revisions.
- Design loops that can hand off to a human cleanly when they hit uncertainty.
# Harness Engineering
"Cost effective harnesses with Fable"
Author: @RLanceMartin
Source: x.com/i/web/status/2…
This article shows how to keep agent harnesses cheap without sacrificing reliability by being deliberate about what gets delegated to the model.
- Design harnesses around minimum viable model calls rather than letting the agent run open-ended.
- Use cheaper models or shorter contexts for validation, routing, and formatting while reserving heavy models for real reasoning.
- Measure cost per successful outcome so optimization targets the full system, not just the prompt.
# Prompt Engineering
"GPT-5.6 Prompting Masterclass (by OpenAI)"
Author: @aiedge_
Source: x.com/i/web/status/2…
This masterclass distills the current OpenAI guidance on prompting into practical patterns that improve model output quality and consistency.
- Be explicit about the role, task format, and success criteria before asking for the answer.
- Use structured delimiters and examples to reduce ambiguity in complex requests.
- Iterate on instructions based on failure modes rather than rewriting prompts from scratch every time.
# Second Brain / Memory Engineering
"Build a memory system that survives across sessions with Fable 5: write, consolidate, recall, apply"
Author: @cyrilXBT
Source: x.com/i/web/status/2…
This article treats memory as an active loop where information is captured, refined, retrieved, and put to use instead of simply stored.
- Separate capture from consolidation so raw notes do not clutter the retrieval layer.
- Build recall around the task at hand rather than returning the entire memory history.
- Close the loop by applying retrieved memory to a real action and feeding the result back in.
"I built a living 24x7 Knowledge Graph in Obsidian that updates itself daily with AI"
Author: @KanikaBK
Source: x.com/i/web/status/2…
This piece demonstrates how a knowledge graph can become a self-updating thinking tool when paired with a daily AI ingestion habit.
- Structure notes as connected entities and relationships rather than isolated documents.
- Automate a small daily review pipeline that identifies new links and updates stale nodes.
- Treat the graph as a reasoning surface for the agent, not just a static reference.
"How to Build an AI Memory System in 30 Minutes (Templates Included)"
Author: @ivanainai
Source: x.com/i/web/status/2…
This piece makes long-term memory approachable by breaking it into a quick-build system with ready-to-use templates.
- Start with a small, structured memory schema instead of dumping every conversation into storage.
- Use templates for read, write, and summarize operations so memory stays consistent across sessions.
- Test memory retrieval against real tasks before scaling the system to more data.
# Jarvis-like Agent Engineering
"Hermes Agent Master Class Part 5: Tools and Toolsets"
Author: @tonysimons
Source: x.com/i/web/status/2…
This installment focuses on how tools and toolsets turn a language model into an agent that can act on the world.
- Group tools into coherent toolsets so the agent chooses from a curated menu rather than a chaotic list.
- Describe each tool in terms the model can reason about, including when not to use it.
- Test tool calls in isolation before wiring them into autonomous loops.
[disclosure: this was written with AI with human edits and article selection. If you would like to see more articles like this, please subscribe to my free newsletter in the bio]
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