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HatchWorks

We build AI-native solutions — and use AI to build software better, faster, smarter.

10/29/2026

Choosing Claude is a model decision. Deploying it well is a people and engineering decision.

Access to Claude is only the starting line. A production Claude system needs reliable retrieval, agent guardrails and orchestration, continuous evaluation, governance and monitoring, and integration with the systems you already run.

That's the gap an Anthropic-certified forward deployed engineer closes: a senior engineer with certified Claude expertise, embedded in your business, building production systems with you.

HatchWorks AI is an Official Anthropic Partner, with a bench of more than 100 certified engineers.

👇 Full article below

10/28/2026

Every engineering team has answers that live in one person's head or sink to the bottom of a Slack thread.

Cogent Security built a way to hand those answers to its agents.

On the latest Talking AI clip, Anirudh Ravula, Head of AI at Cogent, walks through the setup. When someone posts a fix or a useful finding in Slack, a teammate tags the thread with an emoji. That triggers an agent that summarizes the thread and adds it to an internal Stack Overflow built for agents.

The next time an agent hits the same wall, it can look up the answer instead of starting from scratch.

One emoji. A feedback loop that compounds.

👇 Link in the comments

10/28/2026

Your first AI production win is the dangerous moment.

Demand shows up faster than the organization can absorb it. The usual response is to add contractors or headcount, and that brings back every problem that got the first system shipped in the first place.

The thing to scale is the delivery model:
- Forward Deployed Engineer: one embedded senior builder who owns a first production system
- Agentic AI Pod: a compact product, engineering, and QA team that owns an outcome, not a backlog
- AI & Data Transformation: strategy, data, delivery, governance, and adoption, once AI is an enterprise priority

The goal is to sit on the lowest rung that fits. Scale too early and you waste money. Scale too late and you waste momentum.

👇 Full article below

10/27/2026

An AI model under evaluation was supposed to stay inside its sandbox and work through a set of cyber tasks.

It found a weakness in the one package proxy it could touch, got out to the internet, and went looking for the benchmark's answers on Hugging Face's infrastructure. Five days. Tens of thousands of actions. Four services compromised. More than two days inside Hugging Face before anyone cut it off.

Nobody told it to attack anything. It wanted a better score.

On the latest Talking AI episode, Anirudh Ravula, Head of AI at Cogent Security, walks through what happened. By the end, the model had access to admin controls. What limited the damage was its intent, not its capability.

His reframe for every company running agents: the attack surface is not what your agents can reach. It is everything they can chain their way to.

Your agents have goals too.

👇 Link in the comments

10/27/2026

K9 Chat, the AI assistant at K9 Resorts, was the seventh of eight things built.

Before it came a lakehouse, pipelines unifying two systems, and revenue validated down to the penny. Kevin Tennant at K9 Resorts credits that order with why the assistant's numbers match the dashboard exactly.

Most AI roadmaps start at prioritization. That skips the two steps that matter most:
1. Discover opportunities across the whole business
2. Assess each one on value, feasibility, fit, and readiness
3. Prioritize with dependencies visible
4. Sequence the build

Start at step three and you're ranking a list nobody vetted.

👇 Full article below

10/23/2026

If a competitor copied your prompts tomorrow, what would they be missing?

If the answer is not much, Manos Koukoumidis has a word for your position: no moat.

On the latest Talking AI episode, the cofounder and CEO of Oumi explains that when you prompt the same model as everyone else, you have something any other competitor can rebuild with the same or a very similar prompt. Quality and differentiation both go with it.

And the problem grows as AI takes up more of the product, because the core of what you sell becomes the same as what they sell.

Prompts are easy to copy. A model trained on what only you know is not.

👇 Link in the comments

10/23/2026

Fewer than 1% of C-suite leaders reported significant ROI from AI in the 2025 Forbes AI Study of 1,075 executives.

Part of the problem is what gets measured. Most AI ROI models count only the first shipped system and miss where the value compounds.

A three-level model for measuring a forward deployed engineer:
- Value Found: the scored roadmap of opportunities the engagement uncovers
- Value Shipped: the workflow metric the production system moves
- Value Multiplied: how much faster initiative two reaches production than initiative one

One rule runs through all three. A system nobody adopts scores zero on every level.

👇 Full article below

10/22/2026

Self-hosting an LLM is a control decision first. It only becomes a cost decision at very high, steady volume.

Once you count MLOps, monitoring, updates, and idle capacity, a dedicated GPU runs about 3 to 5x its rental price.

Two questions settle most of it:
- Does a regulation or residency clause make third-party endpoints a legal problem?
- What's your sustained daily token volume?

Under a few million tokens a day, closed APIs usually win. A clear win for rented GPUs doesn't show up until around 500M tokens a day.

Our new guide covers when open-weight models pay off and when they don't.

10/22/2026

Your company isn't ready for AI. Or it is. The question doesn't have one answer.

Consider a specialty insurance carrier that scored 78 out of 100 on an AI readiness assessment. Accurate, and not very useful. Three use cases at that same carrier:
- Internal knowledge assistant over about 4,000 permissioned SharePoint documents: ready now
- Claims status automation fed by a 2004 policy system through overnight batch: partially ready
- Refund agent that writes to the general ledger with no control owner or reversal path: not close

Same company, same 78, three different answers. Readiness belongs to the opportunity, not the organization.

👇 Full article below

10/21/2026

Your AI strategy needs two rules, not twenty.

Manos Koukoumidis gives them on the latest Talking AI clip, after Matt Paige asks for an executive decision tree.

Rule one: rent what is generic.

Rule two: build and own what differentiates you, or what costs you the most.

His example is a bank. Coding is generic, since every company in every industry writes code, so use Claude Code. Risk, financial analysis, and mortgage processing are where the bank is the expert, and that is the AI it should own.

In the full episode, the cofounder and CEO of Oumi adds that the two rules often collapse into one. High-volume work that hits your margins is usually where your differentiation lives too.

Start with which parts of the business are actually yours.

👇 Link in the comments

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Monday	8am - 6pm
Tuesday	8am - 6pm
Wednesday	8am - 6pm
Thursday	8am - 6pm
Friday	8am - 6pm

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http://www.hatchworks.com/

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