November 06.2026

2 Minutes Read

Navigate the Coding Harness Landscape: Control Over Features Matters

Futuristic coding harness control options on digital screen.

Choosing Your Coding Harness: A New Approach to Control

The world of coding harnesses has evolved dramatically over the past few years. With numerous options available, from Claude Code to Codex, developers now face an important decision: how to choose the best coding harness for their projects. It’s no longer just about which model can write the best code; it involves understanding the control mechanics of each harness.

Understanding the Control Layers in Coding Harnesses

Each coding harness provides unique control over different aspects of the coding process. As you evaluate options, consider what each harness allows you to manage. For instance, Claude Code provides detailed control on implementation, and its organizational features can enhance your ability to steer development workflows. However, with this control comes responsibility.

The Trade-offs You Must Consider

The balance between control and convenience is pivotal. If a harness like Codex simplifies tasks with built-in verification, it may require you to sacrifice some flexibility. On the other hand, tools that give you extensive control could leave you managing more complex processes independently, impacting your project's efficiency.

Zeroing In on Your Needs

Ultimately, the key question is not which harness is the best but which offers the controls that align with your project’s needs. Does your coding task require intimate management of the runtime environment, or something more automated? In today’s competitive market, understanding these dynamics can dictate your project’s success or failure.

Moving Forward with Confidence

As you delve deeper into the options available, remember that the landscape is evolving rapidly. Keeping your skills sharp is essential, and platforms like O’Reilly are great resources for navigating this complex terrain.

Practical AI Implementation

4 Views

0 Comments

Write A Comment

Please complete the captcha to submit your comment.

Related Posts All Posts

11.03.2026

Harnessing the Power of Loop Engineering for Enhanced AI Performance

Update The Essentials of Loop Engineering in AI In the world of artificial intelligence (AI), the concept of loop engineering is rapidly gaining traction among developers and tech enthusiasts alike. A loop can be defined as an autonomous, self-corrective feedback cycle where AI agents perform tasks, assess their results, and recalibrate their methods until a goal is achieved. This innovative approach can significantly streamline workflows, optimize performance, and adjust to changing requirements. Understanding the Core Principles At the foundation of loop engineering are two essential primitives utilized within systems like Claude Code. The first is the goal primitive, which propels an AI through a structured task until a specific, quantifiable target is reached. The second is the loop rerun, which functions on a fixed schedule or interval to initiate periodic adjustments. This evolution marks a significant shift from earlier manual loop setups and complex setups to a more user-friendly approach. Efficiency Through Diverse Loop Types The Claude Code team categorizes loops in various ways, each designated for particular tasks based on how they are activated and terminated. For instance, a manual loop allows a user to guide the AI through specific prompts while the AI maintains context and adapts on each iteration. By leveraging these differences, developers can ensure they apply the most appropriate type of loop for their unique objectives, enhancing the capabilities of their software projects. Embracing the Future of AI Looping Loop engineering is more than just a technical methodology; it represents a shift in how businesses can operate in an increasingly digital landscape. By adopting these AI-driven processes, organizations can expect greater productivity, reduced errors, and better alignment with their goals. As AI continues to advance, embracing such innovative strategies could very well dictate the competitive edge for industries across the board.

10.29.2026

How Emerging AI Technologies Are Redefining Infrastructure Today

Update New Technologies Are Shaping Infrastructure Today The landscape of infrastructure is quickly changing as emerging technologies like artificial intelligence (AI) and digital transformation redefine how engineers and policymakers approach this vital area. According to Ali Jadbabaie, head of the Department of Civil and Environmental Engineering at MIT, the convergence of these technologies with pressing global issues such as climate change and geopolitical tensions is creating an unprecedented set of challenges. The Urgent Need for Innovative Solutions Unlike previous eras in infrastructure development, today’s demands require a more integrated and holistic approach. Jadbabaie emphasizes that it’s no longer sufficient to rely solely on traditional engineering practices. Instead, a more collaboration-driven strategy is essential, pulling together diverse stakeholders—from governments to private companies—to rethink every aspect of infrastructure from design to financing. The Role of AI and Digital Transformation AI is not merely a tool for efficiency; it also enables smarter decision-making in the design and operation of infrastructure. As the world grapples with an increasing demand for infrastructure—particularly in developing nations—the focus shifts to how we can build these systems sustainably. This era demands that we innovate away from resource-intensive models of the past towards cleaner, more efficient solutions. Global Engagement at the World Infrastructure Forum To address these challenges, Jadbabaie will join experts in Madrid for the World Infrastructure Forum. Here, discussions will center on international cooperation and the urgent need for a paradigm shift in infrastructure development. It is clear that this defining era will rely heavily on collaborative efforts for designing, financing, and managing infrastructure systems that meet the demands of the 21st century. In conclusion, the current transformations in infrastructure require a proactive and integrated approach. Stakeholders must prioritize collaboration and innovative thinking to address both environmental challenges and human needs effectively. As technology continues to advance, the possibility for sustainable infrastructure grows.

10.28.2026

Is Your AI Investment Delivering Returns? Discover the Facts

Update Assessing the Real Value of AI InvestmentIn the fast-evolving landscape of technology, the integration of Artificial Intelligence (AI) into business operations has become a hot topic. A recent conversation with a CIO revealed that despite the remarkable 2.1x increase in productivity from AI tools, the financial implications can be staggering. With top users spending up to $21,000 monthly on AI tokens, leaders must weigh both the costs and benefits of this investment.Understanding the Balance of Costs and BenefitsThe allure of AI is undeniable, but so are its costs. Many organizations are seeing great potential, yet the question remains: Are we truly maximizing our return on investment (ROI)? A study from METR noted engineers perceived AI as speeding up their work by 49%, a dramatic overestimation compared to reality. This illustrates a crucial point: businesses cannot rely solely on anecdotal successes when making decisions about AI integration.The Importance of Accurate MeasurementTo navigate this new terrain effectively, organizations must implement robust measurement systems to assess the real impacts of AI. Not only should businesses track productivity increases, but they also need to consider hidden costs, including reduced staff due to increased automation. When evaluating AI expenditures versus productivity, the drive for efficiency must not eclipse financial sustainability. As discounts and promotions from leading AI companies eventually fade, understanding long-term financial implications is essential.Making Informed Decisions for Future GrowthAs the demand for AI continues to rise, organizations must take a step back to evaluate their strategies. Solutions that suit an IT department may differ vastly from those appropriate for a startup. Future decisions should blend measured advantages with financial realities. Understanding AI tools and their potential applications can provide a strategic advantage. Thus, engaging in thoughtful analysis today will shape the business landscape of tomorrow.

UPDATE

update

Local Business Acceleration

update

update
update
update
update
update
update
update

COMPANY

Home
Categories

646-621-3640

AVAILABLE FROM 8AM - 5PM

City, State

2150 Route 35, Suite 250, Girt, NJ

ABOUT US

We break down the core concepts behind artificial intelligence in a way that's easy to understand, and explore how this transformative technology works.

{"company":" The Week in AI - Local Business Acceleration","address":"2150 Route 35, Suite 250","city":"Girt","state":"NJ","zip":"08750","email":"scott@scotthall.co","tos":"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","privacy":"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"}