Fabian Herhold

AI can research candidates, data tools can map talent markets, and agents can connect sourcing, enrichment, outreach and an ATS.

• But turning those capabilities into a working recruiting system takes someone who combines hiring judgment with the ability to build, connect and improve the stack.
• The post distills that work into six learning areas: AI research and evaluation, talent mapping and enrichment, workflow automation, APIs and MCP basics, outreach and experimentation, and recruiting judgment.
• Readers get skills to practice, not just a job title.

It names four things a reader could build: a researched shortlist from a hiring brief; a fresh candidate pipeline from an existing ATS database; relevant outreach from candidate research; and a better sourcing strategy from interview feedback. The automation starting point is deliberately small: one recurring task in n8n.

Read original

Users describe the outcome; the assistant carries out the steps through connected tools. Herhold says Hyreflow handles the connections, orchestration and training the AI for a given niche, with access to 50+ integrations inside Claude, ChatGPT and similar assistants.

A demo preview for IT recruiters shows an example built inside Claude: researching ten US tech companies with hiring signals—multiple open jobs, fundraising or employees recently departing—then finding hiring decision-makers and work emails.

• The emails are hidden and the remaining accounts blurred.
• The post invites readers to book a personalized demo or try it free.

Read original · Original 2 · Original 3

Not a tab hoarder.

• Or a subscription manager.”
• It lists another database, another enrichment tool, another monthly invoice and another twelve tabs open, then argues that somewhere in that stack a shortlist is waiting to happen.
• Hyreflow’s pitch is to manage sourcing and enrichment through integrations inside the AI the recruiter already uses, so recruiters can focus on the task instead of managing connectors.
• The tension: this still adds a product layer.
• The case depends on it replacing more tools than it adds.

The post says there is a free-forever plan and that users on it are already getting placements. Ask for evidence of the placement claim before using it to assess results.

Read original · Original 2

The subject line determines whether anything else matters.

• The advice is practical: keep subject lines to one to three words where possible, always A/B test, and tailor copy to the situation—re-engaging old contacts, business development to hiring companies, skeptical hiring managers, cold outreach to active hirers, and placement closing.
• The guide warns against sending from a CRM or primary domain and promises a separate post on infrastructure.

The post gives copyable examples in short and longer forms: “[First Name] – saw this”, “hiring [job role]?”, “miss this?”, “need help hiring?”, and “last chance”/“final call”.

An earlier guide shows a shift from channel-level outreach tactics to system-level recruiting automation. The teaching format stays consistent: categories, examples and a stated best-fit situation.

Read original

The metrics: 150 workspaces opened, 64 active that week, and an average new user reaching a first real result in about 24 minutes. The lessons: measure everything, as illustrated by setting up PostHog; track bugs and feedback in Linear, where the AI agent itself files most tickets with details on what broke and how to reproduce it; accept that features, integrations, playbooks and fixes all happen in the same week—“no phase 1, phase 2—it’s all phase now”; and treat activation, not signup, as the real work, building onboarding calls, first shortlists and business-development lists alongside users.

A personal metric in the post: 16 new users had already used all their free credits.

• Growth is described as organic—recruiters telling other recruiters, no ads.
• The post closes with an offer of a demo and free credits.

Read original

Automindz Solutions is the agency Herhold has run for years, in the same space and tackling the same problems; Hyreflow is the software and agent product built on that experience.

• Herhold treats the contrast as a real lesson, not a marketing line: with agency work, a project is delivered, the client is satisfied, and the team moves on to the next project, with some maintenance; with software, something shipped on Monday is being used in unexpected ways by Tuesday.
• An earlier subject-line post signs off with the agency framing—“Grow your pipeline.
• Not your headcount”—and says the agency builds systems that let recruiters scale without scaling manual work.

In the beta post, Herhold says the agency and software share the same space, people and problems, but building an AI harness and selling it as software is a “completely different game.” The concrete contrast is delivery versus iteration: one project ends; the other changes under you within a day.

Read original · Original 2

Anyone asking “What is this person working on, and do they show their work?” should start here.

A build update on Hyreflow, the recruitment AI product, shortly after its beta opened.

• Herhold reports 150 workspaces, 64 active in the week of writing, and about 24 minutes to a new user's first real result.
• The post contrasts running an agency (Automindz Solutions) with shipping software: agency work ends at delivery, while software changes under you within a day.
• Four lessons come with operating details, not slogans: measure everything after setting up PostHog; route every bug and feedback item into Linear, including tickets the AI agent files itself (about 148 so far); run features and fixes in the same week rather than in phases; and treat activation, not signup, as the real milestone.
• It closes with an offer of a demo and free credits, and says all growth was organic, from recruiters telling other recruiters.

For brands planning a campaign

For a recruitment-technology collaboration, this post documents a build-in-public narrative, named tools (PostHog, Linear) and self-reported activation metrics.

• It offers concrete partner angles: a beta-user story, onboarding and activation, or an interview on what shipping software does to an agency owner.
• Treat the numbers as Herhold's claims from that week, not audited results; ask for the post's date, which is not stated.

It predicts a shift in recruiting roles and lists the skills that shift would require, making it the clearest statement of Herhold's point of view.

Herhold argues that GTM Engineering became a talked-about sales and growth role, and predicts Talent Engineering will follow in recruiting.

• AI can research candidates, data tools can map talent markets, and agents can connect sourcing, enrichment, outreach, and the ATS—but someone has to assemble them into a working recruiting system.
• The post defines a Talent Engineer as a recruiter who combines hiring judgment with the ability to build, connect, and improve the systems behind hiring.
• Six learning areas follow, each with concrete tasks: AI research and evaluation (using Claude or ChatGPT to turn a specific hiring need into a research plan); talent mapping and enrichment; workflow automation (starting with one recurring task in n8n); APIs and MCP basics; outreach and experimentation measured by qualified conversations and placements; and recruiting judgment.
• It names four example builds, from hiring need to researched shortlist, and presents Hyreflow as connecting AI assistants to sourcing, enrichment, outreach, and CRM tools.

For brands planning a campaign

A strong thought-leadership asset for a partnership: the skill list could become a workshop, checklist, or co-authored guide to AI-assisted recruiting.

• The essay and product pitch are interwoven: the argument stands on its own, but the closing section is directly promotional.
• A campaign should choose whether to reference the thesis or the product, rather than blur the two.

It shows what Herhold's practical recruiting advice looks like without requiring readers to care about the product.

A practitioner guide to outreach subject lines, for recruiters whose emails compete with 200+ others in a hiring manager's inbox.

• It recommends short subject lines, 1-3 words as a starting rule with longer exceptions, and A/B testing.
• Five categories come with copyable examples and the situations they suit: personal touch (name or company, for client outreach and re-engaging old contacts); the open-job trigger (business development to hiring companies); the curiosity hook (skeptical hiring managers); the problem solver (cold outreach to decision-makers actively hiring); and urgency plays (placement emails and closing deals).
• The closing reminder warns against sending from a CRM or a primary domain and promises a separate post on infrastructure.

For brands planning a campaign

A repeatable educational format for another recruitment topic: examples first, category labels, and a stated best-fit situation for each.

• As channel advice rather than product content, it is a safer basis for co-branded guidance than the promotional posts.
• The post makes qualitative claims, not test results, so any figures would need to come from the partner.

Anyone evaluating a collaboration should review the demonstration format before the pitch.

A short demo preview for IT recruiters.

• Herhold describes building an example business-development workflow inside Claude: researching 10 US tech companies with hiring signals (multiple open jobs, fundraising, employees recently departing) and finding their hiring decision-makers and work emails.
• The post says the emails are hidden and the remaining accounts blurred in the shared material.
• It invites readers to book a personalized demo or try the product free, adding that the workflow can be adapted to niches outside recruiting.

For brands planning a campaign

Shows how Herhold sells: a watched build inside a general AI assistant, with sourcing signals named and outputs partly redacted.

• A partner considering a content collaboration could propose a comparable recorded or live build for its audience.
• The posts do not verify the research output or state a date; ask for validation if the result matters, and for the post's date.
• The value here is the demonstration format, not a measured result.

Read it before assessing a partnership angle.

A compact product post that opens with the pain: recruiters are not tab hoarders or subscription managers, yet they accumulate another database, another enrichment tool, another monthly invoice, and another 12 open tabs.

• It positions Hyreflow as managed sourcing and enrichment with access to 50+ integrations inside AI assistants such as Claude and ChatGPT.
• The vendor handles connections and orchestration, and teaches the AI how to use them for a given niche.
• Herhold says there is a free-forever plan and that some users on it are already getting placements, and links to a signup page.

For brands planning a campaign

Promotional copy offers evidence of positioning, not outcomes.

• The placement claim is attributed to users on the free plan, with no figures or verification; ask for supporting evidence if outcomes matter.
• For partnership planning, the useful material is the stated wedge—consolidating sourcing tools inside an AI assistant—which can be compared directly with a competitor or partner.

They define the role as a recruiter who combines hiring judgment with the ability to build, connect and improve the systems behind hiring.

• AI can research candidates, data tools can map talent markets, and agents can link sourcing, enrichment, outreach and an ATS—but someone still has to make those tools work as a recruiting system.
• The post lays out six skills to develop: using Claude or ChatGPT to turn hiring requirements into a research plan and evaluate results against criteria; talent mapping and enrichment, including de-duplicating profiles and rediscovering candidates already in an ATS; workflow automation, starting with one recurring task in n8n; enough API and MCP knowledge to connect tools without coding; outreach and experimentation measured by qualified conversations, interviews and placements; and recruiting judgment—what makes a strong fit, when data is incomplete, and where a human conversation matters most.
• It closes with four builds readers could produce: hiring requirements to researched shortlist, existing ATS database to fresh candidate pipeline, candidate research to relevant outreach, and interview feedback to a better sourcing strategy.

A numbered skills list paired with named example builds, taking the argument beyond job titles to something readers can try.

For brands planning a campaign

A recruiting-technology brand can use this as an argument piece: it frames tool adoption as a skills shift, not a software purchase, and offers a concrete learning path a vendor could sponsor, host or contribute a module to.

Examples: Source 1

The user describes the outcome; the assistant carries out the steps through connected tools.

• Herhold says Hyreflow combines managed sourcing and enrichment with access to 50+ integrations inside Claude, ChatGPT and similar assistants.
• They handle the connections, orchestration and the work of teaching the AI to use those tools for a given niche.
• A demo post shows the approach in practice: a business-development workflow built inside Claude that researches ten US tech companies showing hiring signals—several open jobs, fundraising and employees recently leaving—then finds hiring decision-makers and their work emails.

Demonstration-first: build a recognizable task in the assistant, blur private data and invite readers to try a personalised version of the same workflow.

For brands planning a campaign

For a recruitment-technology partner, this offers a product narrative to collaborate on: an end-to-end workflow demonstration built around a real, niche-specific task that can become a co-branded demo or case study.

Examples: Source 1 · Source 2 · Source 3

Not a tab hoarder.

• Or a subscription manager."
• It lists the pattern—another database, another enrichment tool, another monthly invoice, another twelve tabs open—and argues that a shortlist is what recruiters are actually waiting to produce.
• Hyreflow’s pitch is that sourcing and enrichment are managed for the user and available through integrations inside the AI they already use, so the recruiter can focus on the task instead of managing connectors.
• The post also notes a free-forever plan and says users on that plan are already getting placements.

The claim that free-plan users already get placements appears without supporting figures in the posts here, so treat it as a positioning claim rather than documented outcome data.

For brands planning a campaign

This is the corpus’s most directly competitive framing: a partner whose product also reduces tool count can reinforce the consolidation argument. A single-purpose tool provider risks being positioned as another line item unless it shows how the product plugs into the orchestration layer.

Examples: Source 1

Its premise: a message competing with 200+ others in a hiring manager’s inbox needs a subject line that sparks curiosity to get opened.

• It gives five categories with real examples in short and longer forms: personal touch, using the person’s name or company; the open-job trigger, tied to a live role; the curiosity hook; the problem-solver, framed around the stress of hiring; and the urgency play, for placements and closing.
• The guidance is to keep subject lines to one to three words where possible and always A/B test.
• A closing note warns against sending from a CRM or a primary domain.

A five-category taxonomy, with a reason each works, short and longer examples, and the outreach situation each suits.

For brands planning a campaign

Useful for a partner seeking copy-level engagement rather than product messaging: the examples can be reused in an outreach template, webinar or co-authored playbook. The infrastructure note also opens the door to a deliverability or sending-domain partner.

Examples: Source 1

Growth is described as organic—recruiters telling other recruiters, with no ads.

• The post draws four lessons: measure everything, illustrated by setting up PostHog and shifting attention from inbox to dashboards; track bugs and feedback in Linear, where the AI agent itself files most tickets with details on what broke and how to reproduce it—148 so far, with most fixed within a day; accept that feature work, integrations, playbooks and fixes all happen in the same week, described as "no phase 1, phase 2 — it's all phase now"; and focus on activation rather than signup, with onboarding calls, first shortlists and business-development lists built alongside users.
• One personal metric: 16 new users had already used all their free credits.

A recurring newsletter-style post pairs headline metrics with named tools and specific numbers, then closes with lessons and an open invitation to a demo.

For brands planning a campaign

This cadence suits a partner seeking visibility into product reality and early traction rather than polished positioning. The activation framing gives partners a shared metric, while the recurring format offers a predictable slot for sponsored or co-hosted build updates.

Examples: Source 1

Automindz Solutions is presented as the agency Herhold has run for years in the same space, solving the same problems; Hyreflow is the software and agent product built on that experience.

• The contrast is presented as a lesson, not just a marketing line: with agency work, a project is delivered, the client is satisfied, and the team moves on to the next project, with some maintenance; with software, something shipped on Monday is used in unexpected ways by Tuesday.
• The earlier subject-line post signs off with the agency framing—"Grow your pipeline.
• Not your headcount"—and says the agency builds systems that let recruiters scale without scaling manual work.

For brands planning a campaign

A partner can pursue services and implementation work on the agency side, tooling and integrations on the product side, or combine the two in one engagement. Partners should clarify which entity and offer they are contracting with.

Examples: Source 1 · Source 2

In a later post, they say, “we've been running Automindz Solutions as an agency for years,” contrasting that model with the software business they are now building.

View example

They promote the product directly, inviting recruiters to sign up for a free plan, book a demo call or DM them. Hyreflow connects AI assistants to sourcing, enrichment, outreach and CRM tools.

View example

No commercial relationship with Anthropic is stated; these are product compatibility references.

View example

Fabian also suggests using ChatGPT or Claude for candidate research and evaluation in recruiting.

View example

Jain's posts describe Fabric moving from interview notes and resume screening into database search, candidate ranking and outreach automation, while Fabian Herhold argues that recruiters should build and connect the systems themselves. Both use examples from a recruiting product to explain where hiring work is heading.

Jain frames the shift through a recruitment software platform's evolution and industry funding moves, such as Metaview's raise. Fabian Herhold's examples are more practitioner-level, showing a named workflow built inside Claude and a build-in-public founder series about a beta product.

Read original · Original 2

Chambers writes about conversational AI for job intake, scoring job requirements, and moving from an ATS to a talent intelligence platform.

• Fabian Herhold writes about connecting AI assistants to sourcing, enrichment, outreach and ATS workflows.
• Both are concerned with where human judgment still matters in hiring.

Chambers's posts are product-announcement and platform-migration arguments for a recruiting platform, focused on intake quality and enterprise tooling. Fabian Herhold's writing is closer to a hands-on guide: a demo of a single workflow inside Claude, plus lessons from launching a beta.

Read original · Original 2

Georgiou writes about quality over quantity, specialist headhunting and understanding the team behind the role. Fabian Herhold writes about giving recruiters better systems so they can do more of the work that matters instead of managing tools.

Georgiou focuses on service quality in specialist sectors such as law, cybersecurity, financial services and healthcare, with little discussion of AI or workflow automation. Fabian Herhold focuses on the tooling and systems layer: AI assistants, sourcing automation and the operational side of recruiting technology.

Read original · Original 2

The posts centre on searching an old database again, looking for adjacent experience instead of an exact keyword, and targeting the small set of companies likely to hold the skill, then reaching out by email, WhatsApp, phone or LinkedIn.

• They also offer a market-narrative angle absent from Herhold’s posts: framing the category through Metaview’s raise and expansion from interview notes toward an AI recruiting coworker, plus Jack & Jill and Juicebox raises, and Fabric’s move from screening and interviews toward a broader hiring platform.
• That suits a partner whose value is database intelligence, candidate ranking or market mapping.

Both posts address the same buyer task with a diagnosis and product promise, not a working demonstration.

• Neither walks through building a workflow, connecting tools or measuring a campaign—the format in Fabian Herhold’s Talent Engineering and demo posts.
• The content also tracks Fabric’s product story and funding context, giving partners more company-voice commentary and less portable recruiter instruction.
• Ask for audience and commercial-term data before proceeding.

Examples: Source 1 · Source 2

The two posts focus on a specific problem: the most expensive recruiting defect happens in the first thirty minutes of a search, when a hiring manager’s objectives are translated into a requisition form and the intelligence is lost.

• The argument is backed by concrete mechanics: a conversational intake that extracts performance objectives from the hiring manager’s own account, challenges stated must-haves against placement data as anti-requirements, scores hiring requirements from 0 to 100 before sourcing starts, and then feeds a longlist and shortlist.
• A second post expands this into a switch case built on an AI-native platform, an 850 million-profile graph, built-in contact data, free data migration and assigned onboarding.
• For a partner selling intake, talent intelligence or database scale, this is a ready-made category narrative with enterprise framing.

Both posts announce a product and argue for replacing an ATS, so the collaboration would carry Loxo’s positioning rather than independent recruitment education.

• The switch-from-your-ATS framing can clash with a partner whose product is meant to sit alongside existing systems.
• Partners seeking tool-agnostic workflow teaching will find no build, integration walkthrough or measurement example like Herhold’s.
• Ask for audience and commercial-term data before proceeding.

Examples: Source 1 · Source 2

The posts build credibility from lived experience: six years running Heilsa after leaving an in-house HR role, placements across 30+ countries, and a deliberate focus on law, cybersecurity and tech, financial services and healthcare, with no generalist shortcuts.

• The second post anchors the pitch in a client review about understanding a team rather than a role on paper, and argues that having sat in the hiring manager’s chair changes how a search is run.
• That suits a partner whose product is judged on quality of hire, sector depth or client trust.

Neither post demonstrates AI sourcing, enrichment, workflow building or campaign measurement, so an automation-focused collaboration would depend on the partner’s product rather than the creator’s demonstrated practice.

• The content makes a service-quality argument, not a systems argument, leaving little to co-create around a tool demonstration.
• Ask for audience and commercial-term data before proceeding.

Examples: Source 1 · Source 2