DEV Community: yaniv zalevas

I've read a lot of DORA dashboards. The number that lies most is lead time.

yaniv zalevas — Fri, 09 Oct 2026 18:07:25 +0000

Every company I've walked into in the last five years already had a DORA dashboard. Someone bought it, someone configured it, someone looked at it once. And in almost every one, nobody could tell me where lead time starts counting.

That's usually the whole problem.

The four metrics, ranked by how much they lie

Lead time — the liar. It lies because it starts wherever your workflow says it starts. Measured from "in progress"? That excludes the two weeks the ticket sat in the backlog waiting to be picked up. Measured from ticket creation? Bulk-closing stale tickets last quarter just cut your lead time in half without shipping anything. And the mid-Jira "waiting for approval" state that eats 80% of the real wait? Counts as zero.

Deployment frequency is the honest one because it's hard to fake — your CI either ran twelve times this week or it didn't.

Change failure rate — quietly gamed. Not dishonest, but elastic: what counts as a failure? If a rollback counts but a hotfix doesn't, you've defined your way to a good number. Teams learn the definition fast.

MTTR — survivorship bias in a chart. You measure recovery for incidents you detected. The slow ones, the ones a customer told you about three days in — they're underrepresented exactly to the degree they're embarrassing.

Deployment frequency — the honest one. Hard to fake, easy to check, boring. Trust it.

The move that makes lead time tell the truth

Read lead time and deployment frequency together. Each alone misleads; the pair diagnoses.

High deployment frequency with long lead time is the most common pattern I see, and it's not an engineering problem. The code ships fine. It sits. Somewhere between "done" and "deployed" there's an approval nobody wants to own — a security review, a release manager, a sign-off that exists in nobody's calendar and everybody's critical path. That's a management problem wearing an engineering costume, and no amount of pipeline optimization will fix it.

Low deployment frequency with short lead time is the mirror image: the team works fast, then batches. Small, safe changes accumulate behind a release train. The risk profile of every release goes up precisely because the process was designed to reduce risk.

What the metric is actually for

A DORA number is only worth having if it triggers the question "why". The number is never the finding — the argument it starts is. Lead time of 14 days tells me nothing. Lead time of 14 days where the code was done on day 2 and waited 12 days for a compliance checkbox tells me everything.

When we built our own engineering-intelligence board (one view over Jira, GitHub, GitLab and Azure DevOps — self-hosted, open-sourced after it changed our own first week in every client), we spent more time on where each clock starts and stops than on any chart. A metrics tool that can't tell you where its clock starts is a poster, not an instrument.

Repo if you want to see where our clocks start: https://github.com/Codpal-Limited/deckgauge — demo with a fictional org at https://demo.deckgauge.com

I'm Yaniv — fractional CTO. Where does your lead time clock start? Genuinely curious, because in five years I've gotten a different answer from every team that ever gave me one.

Three weeks of launching an open-source product: 27 stars, 270 sessions, 0 comments

yaniv zalevas — Fri, 09 Oct 2026 06:48:18 +0000

Three weeks ago we launched Deckgauge in public. I promised the numbers, so here they all are.

The setup: Deckgauge is self-hosted, source-available engineering intelligence — one board over Jira, GitHub, GitLab and Azure DevOps, with DORA metrics and per-engineer workload. We launched it on Product Hunt, a Reddit megathread, dev.to, a couple of listing sites, and got covered by a YouTube channel. Total ad spend: $54.

The numbers, all of them:

GitHub: 27 stars, 3 forks (it was 1 star before launch)
Site traffic: 183 users in September. August gave us 61. That difference is the entire launch: a Reddit comment, four articles, one listing site, and one video. No spike, no front page — just a slope.
Launch article on dev.to: 32 views, 2 reactions
The three articles after it: 10 views, 0, and 10
Comments on all of that combined: essentially zero
Paying customers: 0

What actually moved traffic:

The single best source wasn't a submission form. It was a YouTuber with 55,000 subscribers demoing the board on screen for 38 seconds — The Next New Thing, who found the repo, argued with me about whether it should exist, then demoed it anyway. Direct traffic more than doubled the day after the video.

Every directory and submission channel we paid or applied for produced less than that one unpaid segment.

(One honesty note: the analytics undercounts EEA traffic due to consent gating. If anything, the real numbers are slightly better.)

What I'd do differently:

Publish this numbers post earlier. The weeks of silence in the middle are where you start doubting, and the data says the slope was there the whole time.
Comment in existing threads instead of publishing more articles. Four articles got 52 views; one well-placed comment on a busy thread reaches more people.
Fire the big swing sooner. Show HN is still ahead of us.

Why publish small numbers at all:

Because nobody else does. Every launch story you read is a spike. Nobody publishes the 0-comment weeks, so everyone launching thinks they're the only one failing. Three weeks in, 27 people starred a repo that had 1 star at launch, and I watched every single one arrive.

The repo is here if you want to be the 28th: https://github.com/Codpal-Limited/deckgauge — and there's a live demo with a fictional 25-person org at https://demo.deckgauge.com.

I'm Yaniv — fractional CTO, and I build this in public. Next numbers post in three weeks, whatever they say.

What Jira, GitHub, GitLab and Azure DevOps each reveal about your team — and what they hide

yaniv zalevas — Wed, 23 Sep 2026 13:09:40 +0000

Every company I walk into as a fractional CTO runs a different mix of trackers, and my first week is always the same: pull the data before anyone tells me the narrative. Do that enough times and you notice each tracker has a personality. It admits some truths and buries others.

Here's what six years of walking into other people's tooling has taught me about what each one reveals — and what it hides.

Jira: what the team believes is happening

Jira gives you the richest picture of intended work — sprints, estimates, the epic hierarchy, the whole planned universe. That's also its weakness: it's self-reported. "In Progress" can mean three weeks of nothing. "90% done" is a mood, not a status.

Jira tells you what the team believes is happening. Git tells you what actually happened. Neither one lies, but I've never once seen a department where the two matched — and the size of the gap is the cheapest audit you'll ever run.

GitHub and GitLab: ground truth, with no context

Commits, review latency, who actually reviews whom — this is the real collaboration network, and it never matches the org chart.

The org chart says the team reports to one lead. The review data says every pull request actually goes through one senior engineer. I trust the second document.

What git platforms can't see: priority, scope, or why the work exists. A commit is a fact about a file, not a fact about a plan. You can't read a roadmap out of a commit log, and you can't see the ticket that was quietly deprioritized three sprints ago.

Azure DevOps: the enterprise split-brain

ADO should be the dream — work items and repos in the same tool, data pre-joined. In practice, every team customizes their process template until they speak different dialects of the same tool. The biggest ADO estates I've inherited were also the least readable: inherited template, five workflow states nobody remembers adding, and fields that are mandatory and empty.

If your company runs on ADO, this is not a criticism — it's a warning about what a decade of customization does to a schema.

The punchline: your lead time isn't a metric

Lead time is the metric everyone quotes and almost nobody measures. The clock starts when the ticket moves, and it stops when the pipeline deploys. If those two events live in different tools — and in every company I've worked with, they do — your lead time isn't a metric. It's a guess with decimals.

That's the whole argument for one board over all four trackers. Not "unified dashboards are nice" — that basic, load-bearing numbers cannot be computed inside any single tracker, because each one only sees its half of the lifecycle.

This is why I stopped doing this work with exports. Three CSVs from three tools, a long evening in a spreadsheet, and an answer that's stale the moment anyone moves a ticket.

What we did about it

We built the tool that does the joining — one board over Jira, GitHub, GitLab and Azure DevOps, read-only from every source, self-hosted — and after it changed how our first week in every company goes, we open-sourced it.

Repo: https://github.com/Codpal-Limited/deckgauge — there's a live demo with a fictional org at https://demo.deckgauge.com if you want to see what the four trackers look like as one picture.

I'm Yaniv — fractional CTO and founder of Codpal. What your trackers reveal and what they hide is basically my job description at this point, so I'm curious: which one lies to you most?

The engineer who went quiet is your most important signal

yaniv zalevas — Tue, 22 Sep 2026 18:34:24 +0000

I run a CTO-as-a-service company. Every engagement starts the same way: we walk into an engineering department we've never seen, and we have days to understand it. Before I look at any metric, I look for one thing — the person whose curve went flat.

In every department I've inherited, the person who scared me wasn't the loud one. It was the one who used to ask questions in reviews and stopped. The questions stop about three months before the resignation letter arrives.

What "quiet" looks like in tracker data

Commits drop. PR comments dry up. Issues move slower. Standup answers get shorter. Reviews go from thoughtful one-liners to "LGTM".

None of these trip a single alarm, and that's the problem. Each signal on its own has an innocent explanation. Commits drop — maybe they're in meetings. Comments dry up — maybe the work is straightforward. It's the flat curve across all of them, at once, that you can't explain away. And by the time it's obvious in delivery, you're already a quarter behind on doing something about it.

Why nobody sees it

Here's the part that took me years to internalize: the data was never hidden. It's just split.

The drop is visible in GitHub. The cause is sitting in Jira — they got moved off the interesting work three sprints ago. The context is in the review threads — someone's been crushing their PRs for months. Three exports, three tools, three people who each see one innocent-looking slice. Nobody joins the dots, because joining them requires the GitHub data and the Jira data and the review history to sit in one place where one person can see the whole curve.

That's not a dashboard feature. That's the actual argument for one board over all four trackers — Jira, GitHub, GitLab, Azure DevOps in one view. We built it for exactly this kind of question, and it's the first thing I open in a new company, before any DORA number.

The management trap

The moment you build per-engineer data, someone in the room will want a leaderboard. Don't give them one.

A ranking is the fastest way to make your quiet people quieter — it teaches everyone that the data is being used against them, and the first thing people optimize after that is visibility, not delivery. The guardrail I'd put in writing before rolling any of this out: team-level views by default, individual drill-down only when there's a reason, and the reason is always "someone needs help", never "someone's at the bottom".

The first time you show per-engineer numbers to a team, someone will ask for a ranking. Have your answer ready before that meeting, not during it.

The close

Burnout isn't a wellness metric. It's a leading indicator of delivery risk — the resignation, the handover, the three months of knowledge that walks out the door. The data to see it early has been in your trackers the whole time, waiting for someone to look at it as one picture.

We ended up open-sourcing the board we use for this. Repo: https://github.com/Codpal-Limited/deckgauge — self-hosted, so the per-engineer data never leaves your infrastructure either (if you're going to look at people's curves, the least you can do is keep them on your own server). There's a live demo at https://demo.deckgauge.com with a fictional 25-person org if you want to see what a flat curve looks like before it becomes a resignation.

I'm Yaniv — fractional CTO, ex-IronFX, now building Deckgauge in public. If this post makes you look at one person's curve today, it did its job.

We let AI agents write 1,450 commits of our product. Here's what code review became.

yaniv zalevas — Tue, 22 Sep 2026 03:25:49 +0000

Since April, most of Deckgauge's code has been written by AI coding agents — 4,700 commits total, around 1,450 of them with AI co-author trailers. Every merge is human-reviewed, and the review process we ended up with is not the one we started with. This is what changed.

Deckgauge is a self-hosted engineering intelligence platform — one board over Jira, GitHub, GitLab and Azure DevOps, DORA metrics, per-engineer workload. It's a measurement product, and that matters for this story, because a measurement product has a special relationship with wrongness.

Agents don't write bad code. They write plausible code.

The first assumption I brought to AI-assisted development was that review would be about catching sloppy code. It isn't. The agents never wrote ugly code. They wrote code that looked right and was wrong about the data. That's much harder to review than a syntax error.

A syntax error fails loudly at the first run. A plausible calculation passes the build, passes the type check, sometimes even passes a test — and is wrong about what the number means. In a metrics product that is the worst kind of wrong, because it's confident.

Review stopped being line-by-line

We stopped reading diffs line by line and started reading them assumption by assumption. The middle of an agent diff is usually fine. The edges are where it lies to you: the data contract at the boundary, the aggregation rule nobody stated, the join that quietly changes what a metric covers.

So the human review became three questions, in order: What does this assume about the data? What happens at the boundary — empty repo, zero deploys, missing tracker? And is the number it produces the number we meant? Everything in between — formatting, structure, naming — the agents handle better than I do, and I stopped spending attention there.

The gates came before the humans

The discipline that actually made this work: no agent task reaches a human until it has passed lint, build, typecheck, and the test suite. Not "should have passed" — a script runs them, and the review doesn't start until they're green. That sounds obvious. It took us a while to enforce it strictly, because early on we were reading diffs on top of red builds and learning nothing.

Once the gates were strict, something shifted: review got faster and safer at the same time, because the human was no longer doing work the machine could do. Humans review intent and math. Machines review everything else.

What I'd tell a team starting this

Start with the gates, not the prompts. The quality of agent output is a process problem, not a prompting problem. If you let agent code reach review without a green gate, you will spend your review capacity on things a linter would have caught in four seconds, and you will miss the assumption errors that actually matter — the ones hiding under plausible, well-typed, well-formatted code.

And if your product measures anything for a living: a confident wrong number is the one bug you can't ship. Build the review process around that sentence.

The whole history is public — the repo is https://github.com/Codpal-Limited/deckgauge, and the commit log is the artifact. You can scroll it and see exactly what this workflow produced: the agent commits, the correction passes, the gates. I think it's the most honest case study we have.

I'm Yaniv — I run Codpal, a CTO-as-a-service company. Deckgauge is self-hosted and source-available; there's a live demo at https://demo.deckgauge.com if you want to see what the agents built before you read what they wrote.

I open-sourced the cockpit we used to understand huge engineering departments in days

yaniv zalevas — Sat, 19 Sep 2026 09:07:13 +0000

My name is Yaniv, and I run Codpal, a CTO-as-a-service company. Over the years we've walked into a lot of new companies, and we always needed the same thing: a fast understanding of how engineering actually runs — what's in the roadmap, what the developers are focused on, who the main developers carrying the work really are.

Every engagement started with weeks of meetings to map what the data could have told us in hours.

So we built the tool

Deckgauge pulls Jira, GitHub, GitLab and Azure DevOps into a single data warehouse of development information, and gives you the cockpit picture:

DORA metrics — deployment frequency, lead time, change failure rate, time to restore, computed from your actual delivery data
Team Focus — what every team is actually working on vs. what the roadmap says
Per-engineer workload — who's carrying the delivery, who's drowning
CapEx vs OpEx — how much of engineering is building the future vs. keeping the lights on
One board over all four trackers, with org-chart views

It let us sit with division leads on day one and speak their language — with real data instead of guesses.

The part you might find interesting

Deckgauge was largely built by AI agents (Claude Code with an agent workflow) — human-reviewed on every merge. The tool that measures engineering is itself an experiment in AI-built engineering. Scroll the commit history; it's the artifact.

As featured on YouTube

The Next New Thing (56K subscribers), a weekly show covering the best GitHub repos, featured Deckgauge — demoing the board live and taking on the "why not just Jira?" question:

🎬 https://youtu.be/1fHsIveXRa8?t=1445

We open-sourced it

It worked so well we decided it's a gift for everyone to use:

🔗 Repo: https://github.com/Codpal-Limited/deckgauge
🎮 Live demo (fictional 25-person org): https://demo.deckgauge.com
🌐 Site: https://deckgauge.com

Fully self-hosted — single Docker deploy, nothing leaves your infrastructure. In a world where every engineering-metrics SaaS wants your ticket data in their cloud, we think that matters. License is FSL-1.1 (free for any use except offering it as a competing product; converts to Apache-2.0 in September 2028).

If you're an engineering leader who wants visibility without the vendor cloud, or you just want to see what an AI-agent-built codebase looks like — take it for a spin. Feedback welcome, especially the critical kind.

Cross-post note: this continues the AI-agents journey from my earlier posts here — the tool that measures engineering is itself an experiment in AI-built engineering.

Two Failures, One Lesson: Designing AI Agents for the Bad Day

yaniv zalevas — Tue, 23 Sep 2025 08:51:12 +0000

*TL;DR *
Our first truly organic user tried Agents24x7, hit a known edge-case bug, churned, came back after we fixed it—then ran straight into provider downtime. We shipped a multi-LLM circuit breaker, “never-500” UX, and incident playbook. Takeaway: build for degraded modes first. Consistency > perfection—for users and founders.

Context
We’re building Agents24x7: autonomous content ops for small teams (keyword research → outlines → drafts → internal links → scheduled publish to Shopify/WordPress + socials). We dogfood daily.

Then it happened: our first stranger installed and created her first post.

Failure #1 — The backlog came due
Edge case: “update the same document after feedback” sometimes failed (draft pointer vs published pointer).

We’d parked it while pushing “strategic” features (social posting, multi-lang).

She triggered it on her first edit → fail → bounce.

What we did: agent alerted us → we hotfixed, wrote a human email, apologized, added trial credits, invited her back.

She returned. Confidence restored… briefly.

Failure #2 — Provider downtime at the worst time

We already knew LLMs blip. Our roadmap included a gateway to fail over from Claude → ChatGPT/Gemini gracefully.

She retried exactly during a Claude outage. Murphy clocked in. Another failure surfaced to the user. She bounced again.

Not “our fault”—but absolutely our responsibility.

What we shipped (fast)
1) Multi-LLM Circuit Breaker

Health checks + latency thresholds per provider

Automatic failover to secondary model on soft fails

Idempotent requests + retries (expo backoff)

Quality banner in UI: “Draft created in reduced-capacity mode—please skim.”

2) “Never-500” UX for Content

If a job can’t finish within X sec: save partial + context

Offer “Finish later” or “Try backup model now”

Clear, human error copy + automatic credit comp for severe incidents

3) Observability That Support Can Use

Error taxonomy: USER / AGENT / PROVIDER / NETWORK

Timeline per doc: inputs → model → outputs → publish status

On-call routed from agent events, not just backend logs

4) Release Hygiene

Flags for risky flows (update-in-place, multi-edit)

Dogfood → canary cohort → GA

Lightweight incident playbook (who fixes, who writes, who posts-mortems)

Design principle we’re adopting
Degraded > Dead.
For creation tools, a mediocre draft + clear banner beats a spinner that never ends. Users forgive “reduced mode”; they don’t forgive “I lost my work.”

Founder notes (the headspace part)

Decks promise linear progress. The field delivers dice rolls. Your job isn’t to avoid every rare event; it’s to limit blast radius and recover with dignity—then keep shipping.

Don’t re-order your whole roadmap for every fire. Assign a fixer, keep one person shipping.

Communicate like a human (own it, explain, make it easy to try again).

Build systems that assume the cloud gremlins will visit exactly during a demo.

What we’re seeing now
Update-in-place bug: fixed.

Circuit breaker: live.

Partial-save + clear errors + comp flow: live.

That first organic user? Back and publishing again. We still dogfood daily.

If you’re building agentic tools, steal this checklist

Never-500 core flows (save partial, resume later)
Multi-provider routing with health checks + timeouts
Idempotent jobs + retries + backoff
Explainability artifacts (brief, links, decisions) for reviews
User-readable timelines for support
Feature flags + dogfood + canary
Incident comms templates + automatic credit comp

Why this matters for the future
Agent UX isn’t about sharper prompts—it’s about time and trust. If agents keep the schedule and fail gracefully, small teams can publish like bigger ones and earn compounding organic traffic. That’s a future worth building.

Happy to compare notes on circuit breakers, prompt versioning, and degraded-mode UX.

The Near Future of Content Ops: AI Agents That Keep Time

yaniv zalevas — Fri, 19 Sep 2025 12:15:41 +0000

TL;DR: The next wave of marketing automation isn’t better prompts—it’s autonomous agents that keep schedules. We built one: Agents24x7, a Shopify SEO app (with a WordPress plugin for SEO blog posts) that researches topics, writes, links internally, and automates blog publishing on a cadence you set. The result: consistent output → compounding organic traffic.

Why “consistency” is the real frontier
Search engines reward topical depth and freshness. The constraint for small teams isn’t knowing what to write; it’s shipping on time, every time. We don’t lose to competitors on creativity—we lose on cadence.

That’s a systems problem, not a copy problem.

From LLMs to “clocked agents”
Most teams tried this progression:

Prompt a model → decent draft
Add style guidelines → slightly better draft
Still miss the calendar → traffic stalls

What actually changes the curve is a clocked agent: something that plans topics, drafts, formats, links, and publishes automatically, with clear guardrails and escalation. The agent’s core feature isn’t language—it’s time.

Our approach (and what we’ve learned)
We built Agents24x7 to behave like a dependable teammate:

Learns your brand & products (ingests your one-pager, policy pages, FAQs).
Prioritizes topics by intent + difficulty, focusing on winnable queries.
Drafts SEO-optimized posts (headlines, H2/H3, internal links, meta, FAQ).
Schedules/Publishes to Shopify or WordPress—daily if you want.
Reports back each morning: what shipped, what’s next, what needs a human call.

Think CI/CD for content: small, frequent releases with a visible pipeline.

This turns “remember to post” into infrastructure.

Guardrails > vibes
Automation without boundaries is how brands go off the rails. We harden with:

Style sheet & “never say” list (brand-safe by default)
Fact store for product, shipping, returns (no invented pricing)
Cannibalization checks to prevent overlapping keywords
Escalation rules for sensitive claims and competitor comparisons

Humans review edge cases, not every comma.
Why this matters for the long tail
The future we want: a one- or two-person shop can match the beat of a 5–10 person content team—without pretending. When AI agents for content consistency keep time, small businesses finally compete on ideas and product, not merely output.

This levels the playing field more than any headline “AI feature.”

A tiny playbook you can steal this week

Map 2–3 pillars + 5–8 clusters each (questions customers actually ask).
Commit to a cadence (2–5 posts/week).
Automate the routine (keyword brief → draft → internal links → publish).
Keep a human in the loop for tone, claims, and product nuance.
Measure what matters: posts/week, first-pass approval rate, internal link coverage, non-brand clicks.
Consistency first, sophistication second.

Where we’re headed next

Plug in your data, get repeatable wins: Connect Google Analytics/Search Console and let agentic AI learn from your actual results—then replicate what works across new topics, pillars, and formats.
Inventory-aware content ops: Tie catalog data to editorial choices—prioritize best-selling products, pause low-converters, and automatically remove targeted words/links when items are out of stock.
Social autopilot (with taste): One draft → tailored posts for Facebook and X, scheduled alongside your blog cadence, with UTM tracking back to your store.
Insight → action loop: Dashboards that highlight what’s compounding (queries, posts, internal links) and one-click actions to spin up similar pieces, refresh decaying pages, or expand winning clusters.

Try it (or just borrow the system)

If your question is “How do I grow my organic traffic on my Shopify store?” the unglamorous answer is: ship on schedule around buyer-intent topics. If that keeps slipping, automate it.

App: https://apps.shopify.com/agents24x7

Keywords for discoverability (kept natural): Shopify SEO app, AI agent for content consistency, automate blog publishing, WordPress plugin for SEO blog posts.

Happy to compare notes on pipelines, guardrails, or how to make agents accountable to a clock. The future isn’t chat—it’s clocked.

Automating Blog Consistency with AI Agents

yaniv zalevas — Thu, 18 Sep 2025 15:02:29 +0000

As developers, we’ve all built great projects that never got traction.
Often the missing piece isn’t the product — it’s the marketing.

One of the most reliable ways to drive organic traffic is through SEO and blogging. But here’s the catch:
👉 Writing a blog post takes hours.
👉 Publishing consistently (daily or even weekly) is nearly impossible when you’re coding, shipping, and supporting customers.

Big companies solve this with teams of writers + SEO specialists.
Indie devs and small businesses? We usually don’t have that luxury.

The Problem We Wanted to Solve
We realized the bottleneck wasn’t knowing what to write — it was the time and consistency to actually do it.

If you’re running a Shopify store or a WordPress site, you need:

Keyword research

Competitor analysis

SEO-optimized content

Publishing schedules

Doing that manually is overwhelming.

What We Built: Agents24x7
So we built Agents24x7, an AI agent platform designed to act like a content teammate.

It learns your business profile.

Does keyword + competitor research.

Writes SEO-friendly blog posts.

Publishes them automatically to Shopify or WordPress.

Works 24/7 — no breaks, no burnout.

For us, this solved the biggest gap: consistent content output without extra overhead.

Why This Matters for Devs & Indie Hackers
As builders, we tend to focus on product. But products don’t grow in the dark.
SEO and organic reach are still powerful, but only if you can keep up with content.

Automating blog consistency means:

More visibility for side projects.

More organic traffic for client sites.

Less time spent on non-coding tasks.

Open Question to the Community
I’d love to hear from other devs:

Have you automated parts of your marketing?

Do you think AI agents can fully replace manual blogging?

Or do you prefer to keep content 100% human-written?

⚡️ For anyone curious, we made Agents24x7 available as a Shopify app and a WordPress plugin — so even non-technical teams can use it. But I’m here mostly to share the problem and hear your thoughts on automation in content.

How I Solved the “Consistency Problem” in Shopify & WordPress SEO with AI Agents

yaniv zalevas — Thu, 18 Sep 2025 11:08:40 +0000

One of the hardest parts of running an online business is publishing content consistently.
If you’ve ever tried to grow a Shopify store, WordPress site, or side project, you probably know this pain:

You start with excitement, maybe publish a few blog posts.

Then real life kicks in → managing orders, improving products, handling customer requests.

And suddenly… weeks go by without a single new article.

The result?
Your SEO growth stalls. Your organic traffic flatlines. And Google quietly moves on to sites that publish more often.

Why Consistency Matters
SEO isn’t about publishing one great article.
It’s about publishing relevant, optimized content regularly so Google (and now AI search) sees your site as active, authoritative, and trustworthy.

That’s why the phrase “Consistency is the hardest part of content marketing” resonates with so many founders and developers.

But the truth is → most small teams don’t have the time, budget, or energy to keep it up.

Enter AI Agents for Content
Over the last year, we’ve seen AI help with coding, customer support, even design.
But what if an AI agent could:

Research your competitors
Analyze your business profile
Do keyword research
And then actually write + publish blog posts daily

That’s the problem I set out to solve.

The Agents24x7 Approach
I’m part of the team behind Agents24x7, a platform that builds AI agents designed to take over repetitive business tasks.

For Shopify and WordPress, we created an agent specifically for SEO content.

Here’s how it works:

Keyword Research & Competitor Analysis → The agent scans what works in your niche.
SEO Content Creation → Drafts blog posts optimized for ranking.
Automatic Publishing → Posts directly to your Shopify blog or WordPress site.
Daily Consistency → No off days. Posts go live every day at your chosen time.
In other words: it’s like having a 24/7 content teammate that never forgets to hit “publish.”

Why This Matters for Developers
If you’re building for yourself or clients, you already juggle:

Code maintenance
Bug fixes
Feature requests
Customer feedback

Content marketing usually gets pushed to “later.”

By automating it, you can focus on building, while still growing your organic traffic in the background.

Where to Try It
The app is available as:

A Shopify app → Agents24x7 on Shopify

A WordPress plugin

If you’ve struggled with staying consistent in your marketing, this might save you a lot of late nights.

Final Thoughts
This isn’t about “AI replacing humans.”
It’s about AI augmenting small teams so they can compete with giants who have full-time content teams.

For me, solving the consistency gap in SEO has been one of the most impactful uses of AI agents so far.

I’d love to hear how you handle consistency in marketing for your projects. Do you batch content? Outsource it? Or maybe you’ve tried building your own automation with n8n, Zapier, or custom scripts?

Let’s discuss 👇