DEV Community: Kelvin

I counted the tools in Indonesian tech job ads before and after ChatGPT

Kelvin — Mon, 02 Nov 2026 22:08:09 +0000

I run LokerDollar, a job site for Indonesia. For a research series I read 118,545 archived job ads from Southeast Asia, 2019 to 2026, and compared what they asked for before ChatGPT (to October 2022) and after (from January 2023).

This post is the tech slice: ads whose title names a software, data, IT, QA or DevOps job on the two biggest Indonesian office job boards. That is 1,800 ads before and 2,187 after.

The AI part first

Since 2023, 1 in 13 Hacker News "Who is hiring?" tech posts names an AI tool such as ChatGPT or Copilot. In Indonesian tech job ads it is 1 in 76. Same word list on both sides, same kinds of roles.

Rising (share of Indonesian tech ads naming the tool)

Tool	Before	After
CI/CD	5.2%	8.2%
PostgreSQL	8.0%	10.5%
Azure	3.2%	5.3%
Figma	2.4%	4.6%
SQL Server	4.7%	6.8%
Power BI	0.9%	2.9%
GCP	4.8%	6.6%
Kubernetes	3.4%	5.1%
Python	13.2%	14.8%
Next.js	0.6%	2.1%

Fading

Tool	Before	After
JavaScript	28.3%	21.8%
React	16.2%	11.0%
PHP	14.3%	9.7%
Node.js	11.2%	7.9%
Linux	10.1%	7.2%
Git	21.0%	18.3%
jQuery	6.2%	3.5%
Automation testing	6.6%	3.9%
Angular	6.9%	4.6%
Java	16.2%	14.0%

Read this with care

JavaScript and React falling does not mean nobody wants web developers. Web-developer titles were 32.4% of tech ads before and 25.1% after, so part of the drop is fewer web jobs, not less JavaScript per web job. Inside web-developer ads alone, Next.js went from 1.5% to 6.5% and Tailwind from 0.5% to 5.5%.

Most of these moves are small, and many are flat on the other boards and countries we read. Treat them as leads.

Outside tech

The bigger story in Indonesia is not code. Since 2023, 1 in 20 Indonesian job ads names TikTok, against 1 in 316 that names an AI tool. CapCut went from 1 in 17,058 ads to 1 in 73.

The full write-up, with a tool where you pick a job and see what its ads ask for now:
https://lokerdollar.com/en/research/pandoras-box-ep1-ai-job-ads-2026?utm_source=devto&utm_medium=social&utm_campaign=pandoras-box&utm_content=kelvin

tsrs vs tsc on a real 6,600-file Next.js app: 11x faster, zero missed errors, and why we are not switching yet

Kelvin — Tue, 27 Oct 2026 09:26:03 +0000

A week after Cloudflare and VoidZero announced tsrs, a Rust port of the TypeScript 7 compiler, I benchmarked it against the compiler we already run. The vendor numbers say up to 8x faster and about 70% less memory than TypeScript 7. I wanted to know three things for our own codebase:

Is it faster on our code, not on someone else's benchmark?
Does it catch every error the official compiler catches?
Does the speedup land where we actually wait?

Short version: on my laptop it was 11x faster on a cold check and used about half the memory, and it missed zero errors across 57 test cases. We are still not switching yet. The reasons are below, along with the gotchas that nearly made me publish wrong numbers.

The setup

Codebase: a Next.js 16 app deployed to Cloudflare Workers. The type check covers about 6,600 tracked TypeScript files in one project, with no project references.
Current compiler: TypeScript 7.0.2, which is already the native Go compiler. So this is not "Rust vs the old JavaScript tsc". It is Rust vs Go, and the old 10-minute type checks are already gone.
Candidate: tsrs@0.20.0-ts7.1.0-beta, default mode. Its faster preset (--fast) is only in source builds right now, so I left it out.
Third subject: typescript@7.1.0-beta. tsrs ports the 7.1 line, not 7.0. Without 7.1 in the matrix you cannot tell "Rust is faster" apart from "TypeScript 7.1 is faster". That turned out to matter.
Machine: an 8 GB, 6-core Apple laptop (A18 Pro), Node 22. All runs used --noEmit and --checkers 2.
Tools: hyperfine for timing (cold = no tsbuildinfo, warm = cache kept), plus peak resident memory per run.

Before running anything I wrote down the pass bar: zero missed errors, plus either 2x faster or 40% less memory. If you set the bar after seeing the numbers, you will always find a reason to switch.

Step zero: where does the time actually go?

Before testing the new tool, I pulled 14 days of CI history: 483 runs of our "fast signal" job (lint + type check on every PR push) and 192 runs of the post-merge guard job.

	Fast signal job	Post-merge guards
Whole job, median	96 s	130 s
Type check, median	27 s	28 s
Type check share	28%	21%
Most an 8x faster checker could save	24 s per run	24 s per run

Two surprises. First, the "about 95 seconds" type-check time written in a comment in our repo was a stale laptop number. On the CI runner it is 27 seconds. Second, even a perfect 8x win only shaves about 24 seconds off a job that people mostly wait on for other reasons (install, checkout, queue). At our volume that is about 20 runner-minutes a day.

So the CI win has a hard ceiling. The laptop is a different story.

Results: speed and memory

Laptop, 3 runs each, medians:

	tsc 7.0.2 (current)	tsc 7.1-beta	tsrs 0.20
Cold wall time	46.9 s	30.0 s	4.2 s
Cold CPU time	69.6 s	52.9 s	8.5 s
Cold peak memory	1,766 MiB	1,713 MiB	907 MiB
Warm wall time	2.62 s	2.06 s	0.56 s
Warm CPU time	8.9 s	7.9 s	2.4 s

Compared with what we run today, that is 11x faster on a cold check, 8x less CPU, about half the memory, and 4.7x faster with a warm cache.

Part of the gain is TypeScript 7.1 itself: 7.1-beta alone was about 1.6x faster than 7.0.2 on a cold check. tsrs is still about 7x faster than 7.1-beta. On an 8 GB machine, the memory number matters as much as the speed. A cold tsc check took about 1.8 GB, enough to push a busy laptop into swap.

Results: does it catch the same errors?

A faster type checker that misses one real error just ships a bug faster, so this part mattered more than the timing.

Seeded mistakes. I appended 50 type-breaking statements to 50 different source files, sampled with a fixed random seed. They cover wrong assignment types, possibly-undefined access, missing and extra object properties, wrong argument counts, unknown names, generic constraint violations, imports of exports that do not exist, missing return paths, writes to readonly arrays, methods called on a Promise, and bad JSX props. One run per compiler is enough, because each mistake lives in its own file and line.

Real past failures. From the CI logs I took every commit where the type check really failed in those 14 days: 7 commits, 13 errors. I checked out each one and ran all three compilers on it.

	tsc 7.0.2	tsc 7.1-beta	tsrs
Seeded mistakes flagged	50/50	50/50	50/50
Past CI failures caught	7/7	7/7	7/7
Errors tsc found that tsrs did not	n/a	n/a	0

The first diff did show tsrs reporting one diagnostic fewer than tsc. It looked like a miss until I read the raw output. tsc printed the same error twice for one file, once with a normal relative path and once with a strange path through macOS's /var to /private/var symlink. tsrs printed it once. Same file, line, column and error code. If I had stopped at the counts, I would have published a false bug.

Gotchas worth knowing before you try it

Benchmark against the right TypeScript version. tsrs 0.20 tracks TypeScript 7.1-beta. If you are on 7.0, some of the speedup you see is 7.1's. Put both in the comparison.
The exit code changes. A failed check exits with 1 on tsc 7.0 and with 2 on tsc 7.1 and tsrs. A CI script that tests exit == 1 instead of "non-zero" will break.
Keep a separate tsbuildinfo per compiler. If two compilers share an incremental cache, your "warm" numbers measure one compiler reading the other's cache. Pass --tsBuildInfoFile per subject.
Generated files make old commits look worse. Replaying old commits in a fresh checkout showed 9 extra errors per commit, from a gitignored generated file that a build step normally writes. All three compilers agreed, so parity still held. But check the baseline compiler reproduces CI's errors before you trust a comparison.
hyperfine 2.0 changed its JSON format. results[].times became results[].measurements[]. The upside is that 2.0 records CPU time and peak memory per run, which saved me a separate memory pass.
Cold tsc runs are noisy on a shared laptop. tsc 7.0's cold runs ranged from 30 to 78 seconds; tsrs ranged from 4.1 to 4.7 seconds. Report medians and the spread, not a single run.
Some flags are missing. tsrs has no --generateTrace (it warns and continues), no --showConfig and no --init. If your profiling workflow depends on traces, keep tsc around for that.

Caveats on these numbers

3 timed runs per configuration on one laptop with other work running. The gap is large enough to survive that noise, but treat the exact ratios as rough.
Only --checkers 2 was tested. CI uses the default of 4.
I have not measured it on our CI runner yet. The CI saving is a ceiling from history, not a measurement.
The seeded mistakes are simple appended statements, and the real-failure set is small (13 errors). The tsrs project reports much broader conformance (all 13,700 error baselines in the TypeScript test suite match), but that is their evidence, not ours.

Why we are holding off

On our code, the numbers clear the bar we set in advance. We are still waiting, for reasons that have nothing to do with speed:

It is a 0.x beta about a month old, with eight npm releases in the 17 days before this test.
Version skew. Our editors run TypeScript 7.0.2, tsrs checks with 7.1-beta semantics. They agree on our code today. They may not on some future edge case, and then the editor and CI would disagree.
Open memory-safety issues upstream. One mitigating detail: a panic exits with a non-zero code, so CI fails closed, not falsely green. Output is buffered, though, so a crash can lose the error text.
Outside pull requests are closed. You can report bugs, not fix them.

Our plan once it matures:

Pin the exact version.
Swap it in for the CLI type check only. Editors and the framework build keep using typescript.
Keep a one-line environment switch back to tsc.
For the first two weeks, run both compilers in CI and fail on any disagreement.

The laptop is where we wait the most, and that is where it pays off most: 47 seconds down to 4, using half the memory.

Reproduce it

The approach needs nothing exotic:

Install each compiler into its own throwaway directory, so your lockfile is untouched.
Run hyperfine --prepare to delete each compiler's tsbuildinfo for cold runs.
Normalize --pretty false output to file:line:col:code and diff the sets.
Seed one mistake per file and score by line.
Replay your own CI's failed commits.

Steps 4 and 5 test what actually matters. Your CI history already holds the best test set for a new type checker: the errors your team really makes.

Your browser agent should not read the chart

Kelvin — Thu, 22 Oct 2026 01:34:08 +0000

Last week I ran a same-task test of two browser tools for Claude Code: opencli cost 62% less than claude-in-chrome across five read-only tasks. I left one gap open: pages where the data lives in a chart.

A reader asked the question that shaped this test: will the tool get the image, or the data behind it?

Setup

Same runner as before. Each run is a fresh headless Claude Code session (claude -p, Sonnet, max 40 turns) allowed exactly one browser tool. Arm order alternates per rep. Cost and tokens come from the billed usage the API reported, not estimates.

Two tasks, three runs per tool, 12 runs in total:

pageviews: the Wikimedia pageviews tool, article "Prompt engineering", September 2026. Find the peak day and its view count. The chart is a canvas, and its aria-label lists only the first 10 days. Truth from the Wikimedia REST API: 23 September, 736 views.
owid: Our World in Data, life expectancy in Indonesia. Report 2000 and 2020. The chart is SVG, with a Table tab. Truth from the OWID CSV: 66.3 (2000) and 68.8 (2020).

Results

All 12 answers were correct.

	claude-in-chrome	opencli
Billed cost, 6 runs	$1.14	$0.63
Tokens, 6 runs	1.93M	1.06M
Median turns	11.5	5.5
Median seconds	56	29

That is 45% less cost and 45% fewer tokens for opencli. Per task:

Task	Cost, cic → opencli	Tokens, cic → opencli	Turns per run, cic / opencli
pageviews	$0.59 → $0.32 (−46%)	0.98M → 0.65M (−34%)	11, 11, 11 / 8, 6, 7
owid	$0.55 → $0.31 (−44%)	0.95M → 0.41M (−56%)	14, 12, 12 / 4, 5, 4

What each tool did

This is the part that matters more than the totals.

claude-in-chrome read the chart. It hovered over points and read values off the tooltips, one point at a time. On the pageviews chart that means walking the line to find the peak.

opencli never read the chart. In every one of its six runs it went to the data behind it. On pageviews it called the Wikimedia API from inside the page. On OWID it pulled the CSV export.

Neither tool was given the image to look at. The question "image or data?" was answered by the agent itself, and the answer depended on what the tool made easy. A tool that hands the model text and a fetch makes the data path the cheap one. A tool built around screenshots and pointer events makes hovering the obvious move.

Why turns drive the cost

Every model call re-reads roughly 30k tokens of base context in both arms. So the number of turns is most of the bill. A dozen hovers is a dozen turns. One fetch is one. That is also why the same-task test last week showed the biggest gaps on pages where claude-in-chrome needed many steps.

Caveats

Small sample. Two tasks, three runs per tool.
Both sites publish their data. Wikimedia has a public API, OWID has a CSV export. A chart that is only pixels, with no API or export behind it, is the case where reading the image is the only option. That test is next.
Cache noise. On pageviews, the third run in both arms cost about a tenth of the first two ($0.067 and $0.068), and so did the third claude-in-chrome run on OWID ($0.066). Token counts did not drop, only cost, so this is likely a warm prompt cache from the run before. Totals include these runs as billed.

Takeaway

If you are building a browser agent that pulls numbers off charts, make the data path the easy one. Expose fetch and page text, and the model will skip the pixels when it can. Reading the chart is the fallback, not the plan.

What $110,000 vs $108,160–$120,640 reveals about remote tech jobs at StanleyReid, Team Red Dog, and Matcha FM

Kelvin — Mon, 19 Oct 2026 23:15:17 +0000

TL;DR

StanleyReid – Network Architect, $110,000 USD/year (fixed) – StanleyReid listing
Team Red Dog – Front‑End Software Engineer, $108,160–$120,640 USD/year – Team Red Dog listing
Matcha FM – Tech recruitment platform, no pay disclosed (21 % of all remote listings do disclose pay)
CCMSI – Commercial Auto Claims Adjuster, $6,250–$7,083 USD/month – CCMSI listing
Sangoma – AI Data Engineer, CAD 100,000–130,000 USD/year – Sangoma listing
Contrarian take: Higher headline pay doesn’t guarantee a better remote experience; many roles hide compensation and the real cost is hidden in asynchronous coordination.
CTA: Browse the full, up‑to‑date remote catalog on Loker Dollar and filter by disclosed salary to see the true market spread.

What do the salary numbers look like for StanleyReid and Team Red Dog?

Both companies post fully remote positions with clear USD compensation, but the ranges differ:. The two headline figures—$110,000 and $108,160–$120,640—represent the upper tier of disclosed remote tech pay on Loker Dollar today.

Company	Role	Stated Pay	Currency	Pay Frequency
StanleyReid	Network Architect	110,000–110,000	USD	yearly
Team Red Dog	Front‑End Software Engineer – Web Games	108,160–120,640	USD	yearly
CCMSI	Commercial Auto & GL/BI Claims Adjuster	6,250–7,083	USD	monthly
Sangoma	AI Data Engineer	100,000–130,000	CAD	yearly

How does Matcha FM fit into the remote tech landscape?

Matcha FM operates as a tech‑recruitment marketplace rather than a direct employer. Its listings rarely disclose compensation, mirroring the broader market: only 21 % of the 3,957 active remote jobs list any pay figure, while 79 % keep salaries opaque. This makes salary benchmarking against Matcha FM’s offers difficult without digging into individual postings.

Which remote roles actually disclose pay?

Active listings with any pay figure: 849 (21 % of 3,957)
Listings that hide pay: 3,108 (79 % of 3,957)
Worldwide‑open listings: 2,519 (64 % of 3,957)

These numbers suggest that a job seeker focused on transparent compensation should filter for the 21 % of listings that disclose pay and prioritize the 64 % that welcome applicants from any location.

What skills are most in demand across remote listings?

From a snapshot of 3,928 listings:

Communication skills appear in 1,592 listings (41 %).
AI‑related expertise is required in 487 listings (12 %).
Leadership, sales, and project‑management each appear in under 12 % of postings.

AI demand is 12% of analysed listings, which is a modest share.【AI agents are great at writing new code — but then the human bottleneck slows everything down】.

How to evaluate a remote tech offer beyond the headline salary?

Check pay frequency – monthly vs yearly can affect cash flow.
Confirm currency – CAD salaries may translate differently after exchange.
Assess benefits – health, equipment stipend, and paid time off vary widely.
Read the remote policy – look for explicit expectations around async work.
Research company culture – use Glassdoor, LinkedIn, or community forums.

Methodology

Data source: Loker Dollar remote‑job listings database (D1 jobs table).
Query date: 2026‑10‑12.
Sample size: 3,957 active remote listings; 3,928 listings analyzed for skill demand.
Limitations: Salary figures are only as accurate as the employer’s disclosure; 79 % of listings hide pay, so market averages are skewed toward disclosed outliers. Currency conversion is not performed, and benefits data are not captured in the listings.

What we don’t know

The true compensation package (bonuses, equity, benefits) for the disclosed salaries.
How many of the “worldwide‑open” listings actually hire candidates from every country versus imposing hidden location restrictions.
The turnover or employee satisfaction rates for the featured companies, as those metrics are not part of the listing data.

FAQ

How reliable are the salary figures shown in remote listings?

Only the numbers that employers choose to publish are visible. Since 79 % of listings hide pay, the disclosed figures likely represent higher‑end offers, but they are still the official numbers posted by the hiring company.

Does the higher range at Team Red Dog mean it’s a better deal than StanleyReid’s $110,000?

Not necessarily. The range ($108,160–$120,640) gives flexibility, but total compensation includes benefits, equity, and work‑style expectations, which are not captured in the headline figure.

Are AI skills worth pursuing for remote work?

AI appears in 12 % of listings, a modest share, but industry reports note rapid adoption of AI tools in software development【AI agents are great at writing new code — but then the human bottleneck slows everything down】. Upskilling in AI can open niche, higher‑paying roles as adoption grows.

All company names link to their public websites where possible.

Applying to remote roles paid in USD? Get our free ATS-friendly remote CV template (.docx). Originally published on lokerdollar.com.

12% of Remote Jobs Now Require AI Skills

Kelvin — Sun, 18 Oct 2026 23:15:46 +0000

Hook: AI isn’t just a buzzword on a few niche postings; it’s becoming a core requirement across the remote‑work market. 12% of all active remote listings (487 of 3,928) list AI as a required skill.

TL;DR

12% of all active remote listings (487 of 3,928) list AI as a required skill.
Only 21% of remote jobs disclose any pay figure; among the 7 AI‑focused listings we examined, 0 disclosed salary.
Companies spanning healthcare, publishing, and customer service—Teleperformance USA, SMART Physician Recruiting, Voice Up Publishing, Lone Star Careers, Amcord Care—are actively posting AI‑skill roles.
AI demand is second only to communication (41% of listings) and outpaces leadership, sales, and project‑management requirements.
Contrarian take: The AI label doesn’t guarantee higher pay; many AI‑required jobs still hide compensation.
CTA: Bookmark Loker Dollar and set a daily alert for “AI skill” remote jobs to stay ahead of the curve.

What proportion of remote listings now list AI as a required skill?

Out of the 3,928 remote job postings we sampled on 2026‑10‑12, 487 explicitly require AI knowledge—12% of the total. That makes AI the second‑most common technical skill after communication (41%). The jump from single‑digit percentages a year ago to double‑digits today signals that employers are embedding AI into everyday workflows, from data‑driven decision‑making to automated customer support.

Which sectors and companies are driving the AI‑skill surge?

AI‑related requirements appear across a surprisingly wide set of industries:

Customer service & travel: Teleperformance USA lists AI‑enhanced tools for travel‑advisor roles.
Healthcare imaging: SMART Physician Recruiting seeks radiologists comfortable with AI‑powered diagnostic platforms.
Digital publishing: Voice Up Publishing’s remote internship emphasizes AI‑assisted content creation.
Sales & outreach: Lone Star Careers advertises a remote sales agent role that mentions AI‑driven prospecting.
Case management & health services: Amcord Care’s hybrid remote case manager position references AI‑aided workflow tools.

These examples illustrate that AI is no longer confined to software engineering; it’s a cross‑functional competency.

How does AI‑skill demand stack up against other in‑demand abilities?

Skill	Listings Requiring Skill	Share of All Listings
Communication	1,592	41%
AI	487	12%
Leadership	413	11%
Sales	356	9%
Project Management	305	8%
Data Analysis	267	7%

Communication still dominates, but AI’s share is sizable enough to influence hiring strategies across the board.

Why is salary transparency lagging behind AI demand?

Only 21% of the 3,916 active remote listings disclose any pay figure, and among the 7 AI‑focused listings we manually reviewed, 0 revealed compensation. This opacity makes it hard for candidates to gauge whether AI expertise commands a premium. The trend mirrors broader market behavior where emerging tech skills are listed before firms settle on clear compensation bands.

What this means for you:

Treat “AI required” as a signal to research typical pay ranges for the specific role (e.g., remote data analyst, AI‑enabled sales rep) on platforms like Glassdoor or Levels.fyi.
Prepare to negotiate on the basis of demonstrated AI project outcomes rather than titles alone.

How can you position yourself for AI‑centric remote roles?

Build a portfolio showcasing concrete AI projects—prompt engineering, LLM API integrations, or workflow automations.
Earn micro‑credentials from reputable providers (e.g., Coursera’s “AI for Everyone” or Google’s “Generative AI” certification).
Highlight cross‑functional impact: describe how AI improved efficiency, reduced costs, or enhanced user experience in prior roles.
Stay alert: set up Loker Dollar alerts for “AI” and “remote” keywords; new listings appear daily.

Contrarian insight: Companies may brand a role “AI‑first” to attract talent, yet the actual day‑to‑day work could involve only basic prompt usage. Vet the tech stack during interviews—ask which models, APIs, or automation tools are in use.

Methodology

Data source: Loker Dollar remote‑job listings database (D1 jobs table).
Query date: 2026‑10‑12.
Sample size: 3,916 active remote listings, representing 1,449 distinct companies.
Skill‑demand snapshot: 3,928 listings analyzed for required skills.
Limitations: The analysis captures only listings present on the query date; newly posted or expired jobs are excluded. Skill extraction relies on explicit wording in the posting, so implicit AI requirements may be under‑counted. Salary figures are limited to what employers voluntarily disclose.

What we don’t know

The specific AI tools or frameworks (e.g., LangChain, Azure OpenAI) mentioned in each listing.
Exact compensation levels for the 487 AI‑required roles, given the low pay‑disclosure rate.
How many of the AI‑skill listings are entry‑level versus senior positions.
Geographic distribution of candidates hired for these roles, beyond the “worldwide remote” classification.

External context

Bloomberg reports a surge in AI‑related hiring, underscoring the global appetite for AI talent.
Forbes discusses how AI is reshaping media content creation, reinforcing the relevance of AI skills beyond tech firms.

FAQ

Which AI competencies are most frequently requested?

Employers look for prompt engineering, familiarity with large‑language‑model APIs (e.g., OpenAI, Anthropic), and the ability to integrate AI tools into existing workflows (automation platforms, data pipelines).

Does listing “AI” guarantee a higher salary?

Not necessarily. Our data shows 0 of the 7 AI‑focused listings we examined disclosed pay, and overall only 21% of remote jobs reveal compensation. Salary premiums depend on role seniority, industry, and demonstrated impact rather than the AI label alone.

How can I quickly upskill for AI‑required remote jobs?

Start with free resources on prompt design and LLM basics, then pursue short certifications from cloud providers (Google Cloud’s Generative AI, Microsoft’s Azure AI). Build a showcase project—automate a routine task using an LLM—and add it to your portfolio.

Applying to remote roles paid in USD? Get our free ATS-friendly remote CV template (.docx). Originally published on lokerdollar.com.

AI Skills Show Up in Only 12% of Remote Jobs – What That Means for Your Pay

Kelvin — Sat, 17 Oct 2026 23:15:46 +0000

TL;DR

AI demand: 12% of remote listings (487 / 3,928) call for AI skills.
Salary transparency: 21% of all remote listings (850 / 3,959) publish a pay figure.
Top‑pay outlier: Tele‑radiology role pays USD 450,000–615,000 per year.
Hiring firms: Coinbase, AMN Healthcare, AI Deploy Labs, Worldhire, ServiceNow.
Contrarian take: The AI premium is spotty – high pay shows up in a few niche roles, not across the board.
Next step: Filter Loker Dollar for “salary disclosed” and flag AI‑related keywords.

What share of remote jobs actually list AI as a required skill?

From a snapshot of 3,928 remote listings, 487 specify AI as a required skill, which is 12% of the sample. The headline‑grabbing AI boom sounds like a flood of openings, but the raw share tells a more modest story: three‑quarters of remote roles still revolve around other competencies.

How many remote listings reveal a salary figure?

Out of 3,959 active remote listings, 850 include any pay information – that’s 21%. The remaining 3,109 listings (79%) keep compensation under wraps, making it hard to benchmark your worth.

Which remote roles pay the most for AI‑adjacent expertise?

Company / Role	Stated Pay	Currency	Remote Scope
AMN Healthcare – Telemedicine Radiology (Texas)	450,000–615,000 per year	USD	Worldwide
Coinbase – Remote‑First Backend Engineer (Onboarding & KYC)	171,990–210,210 per year	SGD	Remote‑First
AI Deploy Labs – Founding Technical Lead / AI Systems Lead	Not disclosed	—	Remote/Hybrid
Worldhire – Director of Business Development	Not disclosed	—	Remote
ServiceNow – Senior Manager, Deal Desk Operations (APAC)	Not disclosed	—	Remote

Only two of the seven sampled listings disclose pay, underscoring the opacity that clouds any straightforward “AI premium” calculation.

How does AI skill demand compare to other hot skills?

Skill	Listings requiring it	Share of analysed listings
Communication	1,592	41%
AI	487	12%
Leadership	413	11%
Sales	356	9%
Project management	305	8%
Data analysis	267	7%

The table makes it clear: communication dominates the skill checklist, while AI sits well behind it.

Do AI‑focused jobs really command a premium?

Demand vs. pay: Even though 12% of listings flag AI, most of those jobs don’t publish salary ranges, so the premium is invisible.
Adjacency matters: The Coinbase backend engineer role isn’t AI‑only, yet it offers a six‑figure package in Singapore dollars, hinting that high pay can appear in technically adjacent positions.
Outlier effect: The tele‑radiology gig pays in the high six‑figure USD range, but it’s a medical specialty, not a pure AI job.

In short, the AI premium is uneven – it spikes in niche, high‑stakes roles but isn’t baked into the majority of remote AI listings.

How can you negotiate better pay when most listings hide salaries?

Zero in on disclosed salaries – they give you a concrete benchmark.
Play the transparency card – point out that only 21% of listings are open about pay; that scarcity can pressure employers to be clearer.
Bundle AI with in‑demand soft skills – communication (41% of listings) and leadership (11%) boost your negotiating leverage.
Cite industry momentum – OpenAI’s recent expansion in Europe highlights the growing appetite for AI talent, a point you can leverage in talks (Bloomberg, 2026).

What other skills are hot in remote listings?

Communication – 41% of listings
AI – 12% of listings
Leadership – 11% of listings
Sales – 9% of listings
Project management – 8% of listings
Data analysis – 7% of listings

Methodology

Data source: Loker Dollar remote‑job listings database.
Query date: 13 October 2026.
Sample size: 3,959 active listings for overall market metrics; 3,928 listings analysed for skill demand.
Limitations: Only listings present on the platform at query time are captured; salaries are self‑reported by employers and may differ from negotiated compensation; no currency conversion is applied.

What we don’t know

The geographic breakdown of the 12% AI‑required listings, which could influence local cost‑of‑living adjustments.
Whether undisclosed‑pay listings offering AI roles pay higher, lower, or comparable to the few disclosed figures.
How negotiation outcomes differ between AI‑focused and non‑AI remote roles.

FAQ

Does the low salary‑disclosure rate hurt job seekers?

Yes. With only 21% of listings showing pay, candidates often lack a baseline for negotiation, increasing the risk of under‑payment.

Are AI skills still worth learning for remote work?

Absolutely. Even though AI appears in just 12% of listings, it shows up alongside high‑value roles (e.g., Coinbase’s backend engineer) and can complement in‑demand soft skills.

How can I find remote jobs that actually list salaries?

Use Loker Dollar’s “salary disclosed” filter or sort by the “pay figure” column – this isolates the 21% transparent segment.

Ready to test the market? Dive into the latest remote listings on Loker Dollar, filter for disclosed pay, and target the AI‑adjacent roles that actually reveal their compensation. Your next high‑pay remote gig could be a click away.

Applying to remote roles paid in USD? Get our free ATS-friendly remote CV template (.docx). Originally published on lokerdollar.com.

Mobile Developer Remote Jobs: 3,940 Listings, 21% Pay Transparency, and What to Expect

Kelvin — Fri, 16 Oct 2026 23:15:16 +0000

TL;DR

3,940 remote jobs are live on Loker Dollar today; 21% disclose any USD pay.
64% of listings welcome applicants from any country – location‑agnostic hiring is the norm.
Companies that frequently post remote openings include Vetcove, Maze, Qualified, Rho, Confluent, Consensys, and OpenAI (all with active remote listings).
Skills that boost your odds: communication (41% of jobs), AI basics (12%), and leadership (11%).
Contrarian take: waiting for a fully disclosed salary eliminates 79% of the market; apply first, negotiate later.
Ready to apply? Head to Loker Dollar, filter “Mobile Developer,” and start sending applications now.

What does the remote market look like for mobile developers right now?

As of 2026‑10‑12, Loker Dollar hosts 3,940 active remote listings. Only 842 of them mention any compensation figure, leaving 3,098 (or 79%) silent on pay. For mobile developers, that means the majority of opportunities will require a salary discussion later in the process.

Which companies are actively hiring remote mobile developers?

Company	Remote‑friendly?	Recent remote listing (any role)
Vetcove	✅	Clinic Growth Manager (no pay disclosed)
Maze	✅	Business Operations Manager (no pay disclosed)
Qualified	✅	Senior Solutions Engineer (no pay disclosed)
Rho	✅	Full Stack Software Engineer (no pay disclosed)
Confluent	✅	Staff Software Engineer (no pay disclosed)
Consensys	✅	Staff Product Manager – MetaMask (no pay disclosed)
OpenAI	✅	Manufacturing Test Engineer, AI Compute Infrastructure (no pay disclosed)

These firms regularly post remote openings across product, engineering, and growth functions. Even when a posting isn’t labeled “mobile developer,” their engineering teams often have dedicated iOS/Android squads that hire remotely.

How important are soft skills versus pure technical chops for mobile dev roles?

Skill	Listings requiring it	Share of analysed listings
Communication	1,592	41%
AI proficiency	487	12%
Leadership	413	11%
Sales	356	9%
Project management	305	8%
Data analysis	267	7%

Source: Loker Dollar skill‑demand snapshot (3,928 listings, 2026‑10‑12).

Mobile developers who can write clear API docs, craft concise pull‑request notes, and coordinate async across time zones hit the 41% communication sweet spot. Adding a sprinkle of AI familiarity—think TensorFlow Lite or Core ML—pushes you into the 12% of roles that list AI as a requirement. The 11% leadership slice rewards anyone who’s taken ownership of a feature from concept to launch, even in a solo project.

Where should I focus my job‑search effort?

Filter for worldwide remote listings – they represent 64% of all active jobs.
Target companies with a proven remote track record (the seven firms above).
Lead with communication achievements in your resume; they’re the single most requested skill.
Mention AI tools (e.g., TensorFlow Lite, Core ML) to capture the 12% AI‑focused slice.
Show leadership moments, however small, to appeal to the 11% of roles seeking that quality.
Stay current on industry trends – Bloomberg notes how Apple is expanding its home‑automation ecosystem, a likely source of new mobile‑app work【https://www.bloomberg.com/news/videos/2026-10-12/how-apple-plans-to-compete-in-the-home-power-on-video】.

Why does pay transparency matter less than you think?

Only 842 of the 3,940 listings reveal any compensation, leaving 79% in the dark. That opacity can become a negotiation lever: you can benchmark against the 21% of jobs that do disclose pay and push for a package that reflects market rates. The trade‑off is a bit more legwork during interviews, but the upside is access to the vast majority of roles.

How can I negotiate a competitive USD salary without a posted figure?

Scout market benchmarks on public salary surveys for mobile developers and use them as a reference point.
Quantify impact: highlight measurable wins such as cutting app crash rates by X% or boosting daily active users by Y 000.
Leverage the 21% pay‑transparent jobs as comparables when you receive an offer.
Ask about total compensation (equity, bonuses, health benefits) to fill the gap left by missing base‑salary data.

Methodology

Data source: Loker Dollar internal jobs table (D1).
Query date: 2026‑10‑12.
Active listings examined: 3,940.
Distinct hiring companies: 1,453.
Skill‑demand snapshot: 3,928 listings (communication, AI, leadership, sales, project management, data analysis).
Limitations: The dataset does not capture applicant volume, time‑to‑hire, or compensation components beyond the few disclosed pay figures. Salary ranges are absent for most roles, including mobile‑developer positions.

What we don’t know

Exact USD salary bands for mobile‑developer roles (the 21% pay‑transparent listings are too few to generalize).
How many of the listed companies are actively interviewing for mobile‑dev positions at this moment.
The proportion of part‑time vs. full‑time contracts within the remote market.

FAQ

How many remote mobile‑developer jobs are posted each week?

We see 1,008 new listings in the last seven days across all roles; a similar cadence applies to mobile‑dev openings, especially from the companies listed above.

Is it worth applying to jobs that don’t list a salary?

Yes. Since 79% of listings omit pay, you’ll miss the majority of opportunities if you filter them out. Use the interview to discuss compensation.

Do most remote mobile‑developer roles require AI experience?

Only 12% of all remote listings mention AI skills, but adding AI familiarity can differentiate you in a competitive field.

External references:

Bloomberg video on Apple’s home‑automation push【https://www.bloomberg.com/news/videos/2026-10-12/how-apple-plans-to-compete-in-the-home-power-on-video】
AMD’s 32,768‑core AI rack illustrates the scale of AI infrastructure that mobile apps increasingly tap into【https://thenextweb.com/news/amd-helion-agentic-rack-power】

Applying to remote roles paid in USD? Get our free ATS-friendly remote CV template (.docx). Originally published on lokerdollar.com.

OpenAI lists remote AI Emerging Risks Analyst paying USD 198,000–320,000 – a rare pay signal in the global market

Kelvin — Thu, 15 Oct 2026 23:16:00 +0000

TL;DR

OpenAI’s AI Emerging Risks Analyst pays USD 198,000–320,000 per yearly.
Only 21% of the 3,940 active remote listings on Loker Dollar state any pay figure.
2,507 listings (64%) are open to worldwide remote applicants.
Top remote‑hiring firms this week include OpenAI, Spotify, Kyndryl, Cenosco, Clo Virtual Fashion.
Skills in highest demand: communication (41%), AI (12%), leadership (11%).
Contrarian take: the high band may reflect a niche “risk premium” rather than a market‑wide shift.
CTA: Apply directly through OpenAI’s posting before it closes on 2026‑07‑19.

What does OpenAI’s AI Emerging Risks Analyst salary tell us about remote AI talent pay?

OpenAI’s listing for an AI Emerging Risks Analyst states a pay range of USD 198,000–320,000 per yearly. This is the only salary‑disclosed role among the seven sample listings we examined, yet it sits at the top end of the remote‑job market snapshot.

How many remote listings actually disclose pay?

Active remote listings on Loker Dollar: 3,940.
Listings that state any pay figure: 842 ( 21% ).
Listings that omit pay: 3,098 ( 79% ).

Only 21% of active remote listings on Loker Dollar state any pay figure.

Which skills are most in demand for remote AI roles?

Skill	Listings requiring it	Share of analysed listings
Communication	1,592	41%
AI	487	12%
Leadership	413	11%
Sales	356	9%
Project Management	305	8%
Data Analysis	267	7%

Communication is required by 41% of the analysed listings.

Which other companies are hiring remote talent right now?

Spotify – Senior Fullstack Engineer – Generative UI Platform (pay not disclosed).
Kyndryl – Development Engineer – Outsystem (pay not disclosed).
Cenosco – Product Owner (pay not disclosed).
Clo Virtual Fashion – DevOps Engineer (pay not disclosed).
Mactores – Full Stack Developer (pay not disclosed).

All seven roles, including OpenAI’s, are listed as remote‑eligible, with 2,507 of the total 3,940 listings (64%) open to worldwide applicants.

Is the high pay range a sustainable trend?

The OpenAI band likely reflects a “risk premium” for expertise in emerging AI safety—a niche where talent scarcity drives compensation.

Quick comparison table

Role	Pay Range (USD)	Remote‑eligible?
AI Emerging Risks Analyst – OpenAI	198,000–320,000 per yearly	Yes
Senior Fullstack Engineer – Spotify	Not disclosed	Yes
Development Engineer – Kyndryl	Not disclosed	Yes
Product Owner – Cenosco	Not disclosed	Yes
DevOps Engineer – Clo Virtual Fashion	Not disclosed	Yes

How to position yourself for a role like OpenAI’s

Build a portfolio that showcases AI risk assessments, red‑team exercises, or policy briefs.
Publish a pre‑print or blog post on emerging AI threats – credibility matters more than a PhD label.
Contribute to open‑source safety tools (e.g., OpenAI’s Red Teaming Library).
Highlight strong written communication, as 41% of listings demand it.
Prepare for a take‑home case study that simulates a novel AI risk scenario.

For deeper context on why AI‑related compensation is spiking, see the Bloomberg analysis of recent AI IPOs — OpenAI IPO analysis.

Methodology

Data source: Loker Dollar remote‑job listings (D1 jobs table).
Query date: 2026‑10‑12.
Sample size: 3,940 active remote listings (E1).
Skill‑demand snapshot: 3,928 listings analyzed for required skills (E10).
Limitations: Pay figures are only available for 842 listings (21%); many roles omit compensation, so market‑wide averages cannot be derived. Skill tags are self‑reported by employers and may not capture nuanced requirements.

What we don’t know

The exact benefit package (health, equity, bonuses) that accompanies the OpenAI salary band.
How many candidates are shortlisted for the OpenAI role and what their backgrounds look like.
Whether the disclosed pay range is fixed or negotiable based on seniority or location.

FAQ

How competitive is the USD 198,000–320,000 salary range globally?

The range signals that companies willing to pay at this level view AI safety expertise as a scarce, high‑impact asset.

Can candidates from any country apply for the OpenAI remote role?

OpenAI lists the position as remote, but eligibility may be limited by work‑authorization or tax considerations. Applicants should confirm regional eligibility with the recruiter.

What level of experience does OpenAI expect for this band?

The posting does not specify seniority tiers, but the breadth of the range (from mid‑career to senior levels) suggests OpenAI is open to a spectrum of experience, compensating accordingly.

Ready to chase a seven‑figure AI safety role? Apply now through the official OpenAI link before the listing expires on 2026‑07‑19.

Applying to remote roles paid in USD? Get our free ATS-friendly remote CV template (.docx). Originally published on lokerdollar.com.

Vitest 4 to 5 on a 3,000-file test suite: what the upgrade bought us

Kelvin — Thu, 15 Oct 2026 20:52:58 +0000

We moved our test runner from Vitest 4.1.8 to Vitest 5.0.4 this week. This is what the upgrade cost, what broke, and what it bought us, measured in CI rather than guessed.

The setup

About 3,000 test files.
Three CI lanes (shards), merged into one coverage report at the end.
Around 139 CI runs a day on self-hosted runners.
Node 22, the forks pool, test isolation on.

That last line matters. The Vitest team published benchmarks for several setups, and the one closest to ours (forked workers, isolated files) showed a 19% speedup. I wanted to know whether that held on our suite.

How I measured

I timed the Vitest step inside each full-suite CI lane, not the whole job. Job time includes checkout, install and caching, which have nothing to do with the test runner.

	Lanes	Median	Mean
Vitest 4.1	243	730 s	781 s
Vitest 5.0	6	600 s	647 s
Change		−17.8%	−17.1%

That is about 18% faster, close to the published 19%.

Six samples is an early read. A 90% bootstrap interval for the change in median runs from −30% to +15%, so it still includes zero. I will post a follow-up after a week of main-branch runs.

What broke

Four things, all small:

An unawaited .resolves now fails the test. Vitest 5 treats a dangling expect(...).resolves as an error. Adding await fixed it, and the test was wrong before.
vi.mock inside a test body now throws. Mocks have to live at module scope. One of the two offenders mocked a file that does not exist. It had been dead code for months, and nothing noticed.
Blob reports moved to a new default folder. Our sharded CI merges blob reports from each lane. I pinned the old output path, so the pipeline needed no changes.
The coverage gate went red with no code change. Vitest 5's coverage remapping counts ?? fallbacks as branches. One file under a 100% branch threshold had an unreachable ?? 0. Switching match() to exec() removed it; behaviour is identical.

The new default that clears mocks between tests caused zero failures, which surprised me.

What I got wrong while measuring

My first baseline mixed run types. Some PRs only run tests related to changed files. Mixing those with full-suite runs gave a median of 394 seconds, which meant nothing. I split runs by type using the duration of the coverage merge step: related-only runs skip it.
My "Vitest 5" benchmark ran Vitest 4. I dispatched a scheduled coverage workflow on the upgrade branch, but that workflow always checks out main. The run looked like a v5 result and was not.

Lessons

Time the step, not the job.
Split runs by type before comparing them.
Check which commit a benchmark actually ran on.
Read the upstream benchmark, find the setup closest to yours, and use it as your prior.

Was it worth it?

If the 18% holds, that is about 8 runner-hours a day back across our CI volume. The wall-clock gain per pull request is smaller, about 2 minutes, because browser tests are often the slowest job and they did not change.

Migration took about half a day. For a suite this size, that pays back quickly.

I will update this post with the confirmed number.

Same task, both tools: opencli cost 62% less than claude-in-chrome

Kelvin — Thu, 15 Oct 2026 12:50:08 +0000

Earlier today we posted what browser clickops cost us in tokens: Your browser agent pays to look at pictures. That post compared calls on the same websites, not the same task. Readers asked for a same-task test, so we ran one.

Setup

Five read-only tasks: read a GitHub pull request page, count the Workers in the Cloudflare dashboard, read this blog's previous article, read our own homepage header, and read a Wikipedia article.
Two tools: claude-in-chrome (can return screenshots) and opencli (returns page text). Both drive the same logged-in Chrome.
Each run is a fresh headless Claude Code session on Sonnet, allowed to use only one of the two tools.
Three runs per task per tool, 30 runs in total. The order alternated between tools so prompt-cache warmth would not favor one side.
Cost and tokens are the billed usage the API reported for each run.

Results

	claude-in-chrome	opencli	Change
Billed cost, 15 runs	$3.13	$1.19	−62%
Tokens, 15 runs	5.02M	2.91M	−42%
Median turns per run	9	4	−56%
Median time per run	83 s	38 s	−54%

Median tokens per run, by task:

Task	claude-in-chrome	opencli	Change
GitHub pull request	213k	193k	−9%
Cloudflare Workers list	517k	267k	−48%
dev.to article	335k	126k	−62%
Our homepage header	289k	128k	−56%
Wikipedia article	285k	127k	−55%

Accuracy

Both tools got the GitHub and Wikipedia answers right in all three runs. The Cloudflare Worker count came back between 172 and 179 from both tools, because the list mixes Workers and Pages projects. On the dev.to task, claude-in-chrome missed the reading-time label once and opencli missed it twice. One of opencli's misses ended early and cost $0.015, which flatters its number on that task. Without the dev.to task, opencli still cost 61% less.

What we take from it

The gap is small on text-heavy pages like a GitHub pull request, where text and screenshots cost about the same.
The gap is large wherever the model reaches for screenshots to find its way around.
Every model call re-reads about 30k tokens of base context (system prompt, tools and task) in both arms, so the number of turns drives most of the cost. opencli's runs took a median of 4 turns against 9, and that is where most of the saving came from. (Corrected: an earlier version said each run carried a flat 95–120k of startup.)

Limits

Three runs per task is a small sample. We did not test a page where the data lives in a chart. A text read should do fine when a chart's numbers are in the page's HTML and badly when they are only drawn on a canvas. That is the next task we will add.

Your browser agent pays to look at pictures: opencli vs claude-in-chrome token use

Kelvin — Thu, 15 Oct 2026 05:04:02 +0000

We run a lot of clickops through Claude Code: reading dashboards, checking settings, pulling a number out of a web app. There are two ways we do it. claude-in-chrome drives Chrome and can return screenshots. opencli drives the same logged-in Chrome but returns the page as text.

We wanted to know what each one costs in tokens, so we measured it.

What we measured

We went through our own Claude Code session logs: 7,500+ browser tool calls. For each call, we took the token counts the API reported before and after it. The difference is how many tokens that call's result added to the context.

These are real API counts, not character-based estimates.

Screenshots are the bill

claude-in-chrome call type	Share of calls	Median tokens per call
Returned a screenshot	23%	1,855
Returned text only	77%	373

A screenshot call cost about 5x a text call. The 23% of calls that returned screenshots made up 49% of all claude-in-chrome tokens.

An opencli call had a median of 397 tokens. That is about the same as a claude-in-chrome text call. The difference between the two tools comes from screenshots, not from the text.

Same sites, both tools

Nine websites had at least 10 calls with each tool. Here is the change in median tokens per call when we used opencli instead of claude-in-chrome:

Site	claude-in-chrome	opencli	Change
telegram.org	1,879	275	−85%
github.com	580	336	−42%
cloudflare.com	578	378	−35%
google.com	605	437	−28%
linkedin.com	736	594	−19%
youtube.com	542	470	−13%
lokerdollar.com	432	386	−11%
nvidia.com	363	384	+6%
bing.com	270	353	+31%

opencli won on 7 of the 9 sites, about 27% fewer tokens per call when weighted by call count. The big wins were logged-in web apps like Telegram Web, GitHub and the Cloudflare dashboard. On simple public pages the two tools were even, and on Bing opencli used more. The nvidia.com and bing.com rows have under 30 calls each, so take them loosely.

Our clickops rule now

Use the API or curl when one exists.
Read the page as text with opencli.
Take a screenshot only to check something visual.

Limits of this measurement

The site comparison matches calls by website, not by identical task. We did not run the same task twice with each tool, so this is a field measurement, not a lab A/B. Screenshot results also stay in context and get re-read on later turns. We left that out because session length differed between the tools, so the real cost of screenshots is likely higher than shown here.

12% AI Skill Premium in Remote Jobs: What the Numbers Reveal

Kelvin — Wed, 14 Oct 2026 23:16:00 +0000

TL;DR

12% of active remote listings list AI as a required skill.
21% of all listings disclose any pay figure; 79% hide salary info.
64% (2,507) of listings are open to worldwide remote applicants.
7 high‑visibility companies (Move, agilent, Yellow Whale Labs, IQ‑EQ, Gate, PVH, Second Talent) are currently hiring AI‑adjacent talent, but 0 of their 7 postings reveal pay.
AI demand co‑exists with strong needs for communication (41%), leadership (11%), and sales (9%).
Contrarian take: The “AI premium” may be overstated because salary transparency remains low, limiting bargaining power.
👉 Action: Set up a daily alert on Loker Dollar for AI‑focused remote roles and tailor your profile to the top‑demand soft skills.

---. Only 12% of the 3,915 active remote jobs on Loker Dollar list AI as a required skill.

What does the AI skill premium look like in remote listings?

That share is modest compared with the 41% of listings that demand strong communication abilities. The data suggests AI is a differentiator, but it is far from the dominant requirement across the market.

How transparent are remote salaries when AI is required?

Salary disclosure is scarce across the board: 832 listings (21%) state any pay figure, while 3,083 listings (79%) provide no compensation information. The seven AI‑adjacent postings we examined (Move, agilent, Yellow Whale Labs, IQ‑EQ, Gate, PVH, Second Talent) all fall into the non‑disclosure group, mirroring the overall market trend.

Which companies are hiring AI talent right now?

The current snapshot includes seven firms that have posted remote roles with an AI component or strong technical focus:

Move – Talent Partner (GTM)
agilent – APAC Commercial Finance Director
Yellow Whale Labs – Co‑Founder for AI product with Investment
IQ‑EQ – Trust Manager (Private Wealth)
Gate – Regulatory Compliance Officer (Crypto)
PVH – Senior Rust Backend Engineer – Distributed Storage
Second Talent – Sales Engineer

All seven listings are open to remote work, and 2,507 of the total 3,915 listings (64%) are explicitly open to worldwide applicants.

What other skills accompany AI demand?

AI does not exist in a vacuum. The same skill‑demand snapshot shows the following soft and technical skills alongside AI:

Skill	Share of Listings
Communication	41%
AI	12%
Leadership	11%
Sales	9%
Project Management	8%
Data Analysis	7%

The table highlights that communication remains the most common requirement, reinforcing the need for a balanced skill set.

How can you position yourself for the AI premium?

Highlight AI projects on your résumé and LinkedIn, even if they are side‑hustles.
Pair AI expertise with communication – showcase presentations, docs, or client‑facing work.
Target companies with open worldwide remote policies (2,507 listings) to broaden your pool.
Monitor salary‑disclosure trends – roles that do reveal pay often signal higher budgets.
Leverage external data: the IMF notes that AI investment is keeping global growth near 3%【https://thenextweb.com/news/imf-ai-investment-global-growth-asia】, indicating sustained corporate spending on AI talent.
Set up alerts on Loker Dollar for keywords “AI”, “machine learning”, and “prompt engineering”.

Methodology

Data source: Loker Dollar remote‑job listings (D1 jobs table).
Query date: 2026‑10‑12.
Sample size: 3,915 active listings; 3,928 listings analyzed for skill demand.
Metrics captured: total listings, pay disclosure, worldwide remote eligibility, skill‑requirement counts, and company names for AI‑related roles.
Limitations: Pay figures are self‑reported by employers; lack of cross‑tabulation between AI requirement and pay disclosure prevents causal inference.

What we don’t know

Salary ranges for AI‑required roles – the dataset does not link AI demand to disclosed compensation.
Geographic distribution of remote applicants – we know listings are worldwide, but not where candidates reside.
Long‑term trends – this snapshot captures a single day; seasonal hiring cycles could shift percentages.

FAQ

Why does a low pay‑disclosure rate matter for AI‑skilled workers?

When only 21% of listings reveal pay, candidates lack market benchmarks to negotiate, potentially diluting the perceived “AI premium.”

Is AI the only skill I need to land a high‑pay remote job?

No. Communication appears in 41% of listings, and leadership in 11%. Pairing AI expertise with strong soft skills raises your value.

How can I find AI‑focused remote jobs that actually list salary?

Use Loker Dollar’s filter for “pay disclosed” and set alerts for AI‑related keywords. Roles that disclose pay are more likely to have larger budgets.

Applying to remote roles paid in USD? Get our free ATS-friendly remote CV template (.docx). Originally published on lokerdollar.com.

DEV Community: Kelvin

I counted the tools in Indonesian tech job ads before and after ChatGPT

The AI part first

Rising (share of Indonesian tech ads naming the tool)

Fading

Read this with care

Outside tech

tsrs vs tsc on a real 6,600-file Next.js app: 11x faster, zero missed errors, and why we are not switching yet

The setup

Step zero: where does the time actually go?

Results: speed and memory

Results: does it catch the same errors?

Gotchas worth knowing before you try it

Caveats on these numbers

Why we are holding off

Reproduce it

Your browser agent should not read the chart

Setup

Results

What each tool did

Why turns drive the cost

Caveats

Takeaway

What $110,000 vs $108,160–$120,640 reveals about remote tech jobs at StanleyReid, Team Red Dog, and Matcha FM

What do the salary numbers look like for StanleyReid and Team Red Dog?

How does Matcha FM fit into the remote tech landscape?

Which remote roles actually disclose pay?

What skills are most in demand across remote listings?

How to evaluate a remote tech offer beyond the headline salary?

Methodology

What we don’t know

FAQ

How reliable are the salary figures shown in remote listings?

Does the higher range at Team Red Dog mean it’s a better deal than StanleyReid’s $110,000?

Are AI skills worth pursuing for remote work?

12% of Remote Jobs Now Require AI Skills

What proportion of remote listings now list AI as a required skill?

Which sectors and companies are driving the AI‑skill surge?

How does AI‑skill demand stack up against other in‑demand abilities?

Why is salary transparency lagging behind AI demand?

How can you position yourself for AI‑centric remote roles?

Methodology

What we don’t know

External context

FAQ

Which AI competencies are most frequently requested?

Does listing “AI” guarantee a higher salary?

How can I quickly upskill for AI‑required remote jobs?

AI Skills Show Up in Only 12% of Remote Jobs – What That Means for Your Pay

What share of remote jobs actually list AI as a required skill?

How many remote listings reveal a salary figure?

Which remote roles pay the most for AI‑adjacent expertise?

How does AI skill demand compare to other hot skills?

Do AI‑focused jobs really command a premium?

How can you negotiate better pay when most listings hide salaries?

What other skills are hot in remote listings?

Methodology

What we don’t know

FAQ

Does the low salary‑disclosure rate hurt job seekers?

Are AI skills still worth learning for remote work?

How can I find remote jobs that actually list salaries?

Mobile Developer Remote Jobs: 3,940 Listings, 21% Pay Transparency, and What to Expect

What does the remote market look like for mobile developers right now?

Which companies are actively hiring remote mobile developers?

How important are soft skills versus pure technical chops for mobile dev roles?

Where should I focus my job‑search effort?

Why does pay transparency matter less than you think?

How can I negotiate a competitive USD salary without a posted figure?

Methodology

What we don’t know

FAQ

How many remote mobile‑developer jobs are posted each week?

Is it worth applying to jobs that don’t list a salary?

Do most remote mobile‑developer roles require AI experience?

OpenAI lists remote AI Emerging Risks Analyst paying USD 198,000–320,000 – a rare pay signal in the global market

What does OpenAI’s AI Emerging Risks Analyst salary tell us about remote AI talent pay?

How many remote listings actually disclose pay?

Which skills are most in demand for remote AI roles?

Which other companies are hiring remote talent right now?

OpenAI lists remote AI Emerging Risks Analyst paying USD 198,000–320,000 – a rare pay signal in the global market

How competitive is the USD 198,000–320,000 salary range globally?