IT Staffing Archives - SMASH

Hiring Faster Doesn’t Mean Growing Better Anymore

Sofía Delgado — Tue, 05 May 2026 19:55:02 +0000

Hiring Faster Doesn’t Mean Growing Better Anymore

Everyone is talking about layoffs in tech.

Headlines focus on numbers—who’s cutting, how many roles, which companies.

But almost no one is talking about what’s happening underneath.

Because this is not simply a contraction.

It’s a reallocation.

Where is the budget going?

Over the past few years, companies invested heavily in growth.

Hiring faster was the strategy.
Scaling teams was the priority.

More people meant more output.

That equation is no longer holding.

Today, companies are redirecting massive budgets into AI infrastructure and capabilities—with industry spending projected in the hundreds of billions this year.

And that shift is expensive.

So companies are making trade-offs.

Not because they don’t need talent.
But because they need to fund what’s next.

Layoffs are real—but they don’t tell the full story

So far in 2026, tens of thousands of tech workers have been impacted by layoffs, making it one of the most aggressive reset periods in recent years.

But those cuts are not simply about reducing workforce.

Many companies are:

investing heavily in AI infrastructure
reallocating budgets from headcount
optimizing cost structures

Even executives have openly acknowledged the trade-off between people and AI investment.

Tech layoffs surpass 90,000 in 2026

Hiring didn’t stop. It changed.

From the outside, it may look like hiring has slowed down across the board.

In reality, it has become more selective, more intentional, and more strategic.

Some companies are still hiring—especially in AI-related areas—while restructuring elsewhere.

And demand for engineering talent is not disappearing.
It’s evolving.

The pressure hasn’t changed—only the constraints have

Engineering teams are still expected to deliver.

Customer expectations haven’t dropped.
Product roadmaps haven’t slowed down.

But budgets are tighter.
And priorities are shifting.

This creates a tension that didn’t exist before:

👉 Maintain output
👉 Control costs
👉 Invest in AI

All at the same time.

Why hiring more is no longer the default answer

In a growth-driven market, the solution was simple:
hire more people.

Today, that approach creates new problems.

More hiring means:

higher fixed costs
more operational complexity
less flexibility to invest elsewhere

In many cases, it reduces the ability to adapt.

That’s why the advantage is no longer in team size, but in team structure.

What’s actually working now

The teams navigating this shift successfully are not necessarily the largest ones.

They are the ones that:

stay lean and efficient
maintain control over their operations
and create flexibility in how they allocate resources

Instead of expanding headcount traditionally, some are rethinking how their teams are built.

They are introducing additional capacity that integrates directly into their organization, without changing how they operate internally.

This allows them to:

maintain delivery
optimize overall team costs
and free up budget for strategic initiatives like AI

All without compromising quality or control.

A shift in mindset

This is the part most people are missing.

The conversation is no longer about growth at all costs.

It’s about sustainable performance under new constraints.

The companies that will move faster in this next phase are not the ones hiring the most.

They’re the ones that are:

more intentional with how they build teams
more efficient with how they use resources
and more prepared to invest in what’s next

Final thought

Tech is not slowing down.

It’s evolving.

And in this environment, hiring faster doesn’t mean growing better anymore.

It means understanding how to balance delivery, cost, and innovation—all at once.

At SMASH, we’re seeing this shift firsthand.

Teams are not looking to grow faster.
They’re looking to stay efficient while building what’s next.

That means:

maintaining delivery with high-quality talent
optimizing overall team costs
and creating the budget flexibility needed to invest in AI

All while keeping full control of their teams.

If you’re navigating this balance, there are new ways to structure your team that don’t require trade-offs between cost and performance.

Companies investing in AI still need teams to keep the business running.

The question is how to create the budget without sacrificing execution.

Explore how SMASH helps organizations reduce operational costs through embedded nearshore teams.

The post Hiring Faster Doesn’t Mean Growing Better Anymore appeared first on SMASH.

Case Study: SMASH – Bridging Talent and Opportunity Across Borders

adminf07 — Mon, 16 Jun 2025 17:06:50 +0000

Overview

SMASH is redefining how U.S. companies scale tech teams by connecting them with exceptional talent
from Costa Rica and Colombia. With a people-first, employee leasing model that emphasizes integration over outsourcing, SMASH helps companies quickly and seamlessly fill hard-to-hire roles with long-term team members — not contractors.

Challenge

Many U.S. companies face a critical shortage of qualified technical talent. With over 1.2 million unfilled IT roles, hiring managers are under pressure to scale teams without compromising on quality or culture.

Solution

SMASH offers a unique nearshoring model: placing vetted, high-performing Costa Rican and/ or Colombian professionals as full-time embedded team members — not just contractors or outsourced labor. By managing the hiring, vetting, and onboarding process, SMASH ensures both speed and quality while creating true cultural and operational alignment.

Results

Extra Space Storage scaled from 5 to 30 developers since 2019
Customer retention rates of over 80% over the past 6 years
Growing from 2 to 10 engineers at SILAC company.
Seamless team integration and accelerated product development

Testimonials

“It’s hard to find good security coders, and we were able to have the ones we needed through SMASH
immediately. They were also very good at helping the rest of the team with the whys and the hows.”
— Christina Belnap, CSO, SILAC

“When I tell SMASH I need technical talent, I trust that they will find exactly what I need.”
— Dan Lauter, CEO & VP of Operations Health Catalyst

“SMASH does a really good job at finding top technical talent.”
— David Callahan, CTO, Experlogix

“The technical skills were solid, and the cultural fit was mutual.”
— Ken Myers, Manager of Data Engineering, Eptura

“SMASH played a significant role for us. They enabled us to scale quickly through skilled talent.”
— Sam Belnap, COO, SILAC

“We couldn’t have scaled this quickly without SMASH. They helped us build software and solutions we couldn’t have done on our own.”
— Rich Keeler, VP of Software Engineering, Extra Space Storage

“SMASH does the legwork. It felt like shooting fish in a barrel — every candidate was hirable. We just had to pick who we liked best.”
— Jeff Scott, SVP Technology, Extra Space Storage

Future Vision

With a proven model and high customer satisfaction, SMASH is poised for exponential growth. The leadership team is focused on expanding sales, building strategic partnerships, and scaling the unique model that clients already love.

“We’ve built an engine that can support massive demand. The future is growth — and we’re ready.”
— Matt Phillipps, COO, SMASH

The post Case Study: SMASH – Bridging Talent and Opportunity Across Borders appeared first on SMASH.

6 GitHub Repositories to Master Large Language Models

adminf07 — Thu, 22 May 2025 15:44:17 +0000

If you are not familiar with large language models (LLMs) today, you may already be falling behind in the AI revolution. Companies are increasingly integrating LLM-based applications into their workflows. As a result, there is a high demand for LLM engineers and operations engineers who can train, fine-tune, evaluate, and deploy these language models into production.

In this article, we will review 6 GitHub repositories that will help you master the tools, skills, frameworks, and theories necessary for working with large language models.

1. brexhq/prompt-engineering

This repository is a goldmine for learning prompt engineering, one of the most critical skills for working effectively with LLMs. It provides tips, tricks, and examples to help you craft better prompts and get the most out of models like GPT-4o.

Why it is important:

Focuses on practical techniques for optimizing prompts.
Includes examples for diverse use cases, such as summarization, coding, and creative writing.

2. mlabonne/llm-course

This repository offers a comprehensive course on LLMs, designed for learners of all levels. It includes tutorials, projects, and hands-on exercises to help you understand and apply LLMs effectively.

Why it is important:

Covers both theoretical foundations and practical applications.
Perfect for beginners and professionals looking to deepen their knowledge.

3. Hannibal046/Awesome-LLM

This is a complete list of resources related to LLMs, including research papers, tools, frameworks, and tutorials. It is a one-stop shop for exploring the LLM ecosystem and staying updated on the latest advancements.

Why it is important:

Includes resources on training, evaluation, and serving LLMs.
Regularly updated to include new models, tools, and research.

4. WooooDyy/LLM-Agent-Paper-List

This repository is a treasure trove of research papers on LLM-based agents. It is perfect for those interested in cutting-edge AI applications that use AI agents to improve capabilities of LLMs.

Why it is important:

Stay up-to-date with the latest research on LLM-based agents.
Ideal for academics and professionals exploring LLM agent applications.

5. avvorstenbosch/Masterclass-LLMs-for-Data-Science

This repository focuses on integrating LLMs into workflows. It provides an ebook-style introduction to various topics such as prompt engineering, local LLMs, retrieval-augmented generation (RAG) problems, and more. Furthermore, it includes exercises with solutions for you to practice your learning.

Why it is important:

Learn to leverage LLMs in technical projects.
Tailored for data scientists looking to expand their skill set.

6. Shubhamsaboo/awesome-llm-apps

This repository is a collection of awesome LLM-based applications, showcasing real-world use cases built with OpenAI, Anthropic, Gemini, and open-source models. It also highlights AI agents and retrieval-augmented generation (RAG) systems.

Why it is important:

Explore real-world applications of LLMs.
Get inspired by unique use cases and easy to use frameworks.

Conclusion

Mastering LLMs requires a blend of theoretical knowledge, familiarity with modern tools, and hands-on practical experience. The 6 GitHub repositories covered in this blog offer all three by introducing you to cutting-edge AI frameworks, providing valuable resources, papers, and tutorials, and guiding you through exercises and projects to build your own LLM-based applications. Additionally, these repositories are regularly updated, helping you stay current with advancements in LLM applications, AI agents, and frameworks.

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