Full Tech Ahead

In this episode of "Full Tech Ahead," host Amanda Razani interviews Chris Yates, SVP of Product, Design and Engineering at Pantheon. They discuss the critical "trust factor" and governance bottlenecks emerging as organizations rapidly adopt AI tools. 

Yates explains that while AI has massively accelerated the velocity of creating code and content, companies are hitting a wall because their existing review and security processes cannot keep pace. This friction often drives employees toward "Shadow IT" and side-door shortcuts. 

To bridge this gap, Yates advocates for building next-generation scaffolding that treats content and code as a unified substrate. By embedding corporate guidelines, engineering rules, and design systems directly into custom AI skills, organizations can achieve high-fidelity prototyping, enforce uniform brand voice, eliminate "AI slop," and maintain essential human-in-the-loop oversight through staging and replica environments.

Key Quotes

• "Pantheon is, as we say, where the web works. So we are focused on enabling organizations to build and ship on the web at scale."
• "We've kind of hit this point of like, well, now we have to go push this all through the processes that we put up for good reason to create governance... moving the velocity of creation into the missing pieces of governance."
• "I can ask Claude... to create me a new website... and it might be beautiful... but then when I need to go change it, if I don't have the exact domain expertise, it becomes really difficult."
• "Don't wait on the next step, which is how do we drive governance and how do we put these guardrails on how we're doing things?"

Takeaways

• Bridge the Velocity-Governance Gap: The core bottleneck in enterprise AI adoption isn't generation speed, but approval speed. Organizations must build automated scaffolding and pipelines capable of auditing AI-driven code and content variations at the same rate they are generated.
• Combat Shadow IT with Soft Guardrails: Employees will naturally take shortcuts to offload mundane toil. Instead of issuing strict bans, leaders should provide "soft guardrails" by standardizing tools and seeding internal AI systems with custom skills, corporate rules, and style guides.
• Bake Brand Voice into Design Systems: To prevent dry, generic "AI slop" from degrading corporate messaging, integrate communication standards directly into your engineering and design infrastructure. This ensures automated code components and text elements automatically adapt to the brand's exact tone and uniformity.
• Mandate Out-of-Production Human Review: While AI is highly effective at highlighting drift or running static checks, human oversight remains irreplaceable. Enterprise applications require non-production staging environments (replicas of the production fleet) to visualize and verify the "before and after" of AI-driven changes before going live.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Sarah Edwards, CPSO at Kantata. They discuss the strategic implementation of AI in the services industry (consulting firms, agencies, and B2B IT service teams). 

Edwards argues that measuring AI success solely through the lens of traditional productivity and speed is a massive mistake for services firms, as charging by hours while simply doing tasks faster inevitably leads to a financial "race to the bottom." 

Instead, she advocates for shifting toward an AI-native operating model that powers the "expertise economy." 

Key Quotes

• "Measuring productivity [is] the wrong way to measure AI success... if I'm just delivering things faster and faster, traditionally billing my time based on hours or days, well, how am I growing my revenue? That just becomes a race to the bottom."
• "Traditionally, expertise has been reliant on tribal knowledge... AI is disrupting all of that. For the first time, we can really compound that expertise across your business."
• "AI for me is not just about getting faster. It's how do I get better? Because unless I get better... I'm not going to win."
• "In services, quality has been something we've always struggled to measure... Now with the help of AI, we can truly start to gather and measure [sentiment] during project delivery."

Takeaways

• Ditch the Speed Metric for Quality: In professional services, utilizing AI to execute work faster shrinks billable hours without adding value. Firms must stop treating AI as a siloed efficiency tool and start measuring leading indicators of revenue growth, margin improvement, and transformed service delivery.
• Capitalize on Compounded Expertise: Historically, consulting firms were constrained by "heroics" and individual expertise, which created an operational ceiling. An AI-native model unlocks years of hidden project context and conversation logs, instantly upskilling every consultant to the level of the firm's best performer.
• Automate the Sales-to-Delivery Handover: One of the largest operational friction points is when sales teams "throw a project over the fence" to the delivery team. AI agents can eliminate this silo by parsing entire sales-cycle call data into comprehensive briefs, ensuring scope, stakeholder concerns, and project requirements are perfectly aligned.
• Transition to Outcome-Based Metrics: Instead of tracking static, trailing metrics like "on time" and "on budget" at the end of a lifecycle, firms can use AI to track real-time qualitative health indicators, such as client sentiment, delivery team mood, and continuous project drift, while the work is actively in flight.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Max Reele, VP of Delivery at Rise8. They discuss outcome-driven software delivery in high-compliance sectors, specifically focusing on defense tech and gov tech. 

Reele outlines that while AI and agentic assistance allow engineering teams to deliver a much higher quantity of code, the ultimate focus must remain heavily on quality and mission outcomes. 

Drawing from his 20 years of government experience, he warns against common tech project failure modes, such as the "Big Bang" release theory—attempting a hard cutover to completely replace a massive legacy system all at once. 

To combat this and prevent deepening organizational silos, Rise8 advocates for rigorous corporate upskilling, working backward from strict mission metrics, and conducting biweekly demos of working software. 

Furthermore, Reele champions "Extreme Programming" and engineering pairing to safely ground AI agents and prevent codebase hallucinations.

Key Quotes

"At Rise8, we're defense tech and gov tech focused... we build mission unique software for any mission... specifically in high compliance industries."

"Whether it was all hands on keyboard developing the code, or whether it was assisted with Agentic development, the outcome still needs to be the outcome."

"Everybody can become builders with agentic assistance in your development effort, but not everybody's really great builders. And it takes the seasoned software engineers to understand how to interact with the AI agents."

"Please just stay focused on the mission you're trying to improve and let the business operations follow."

Takeaways

Implement "Extreme Programming" with AI: AI agents are flooding codebases with volume, but they can hallucinate or even falsify data to artificially pass test cases. Organizations must pair seasoned, senior engineers with junior developers to continuously audit, test, and safely prompt AI agents, keeping code reliable.

Reject the "Big Bang" Release Trap: Attempting a sudden, full-scale replacement of a massive legacy operating system of record causes immense friction, timeline overruns, and project cancellations. Instead, break modernization efforts down into small, digestible bites and integrate users gradually throughout the journey.

Enforce Biweekly Software Demos: The ease of localized AI tooling risks driving engineers into deeper silos. To force collaboration and structural alignment, teams must pull their features, security hygiene, and technical debt together into a coherent, working software demo presented to primary stakeholders every two weeks.

Encourage Engineering Enablement: Business leaders must shift their mindsets regarding workforce upskilling. When an engineer raises their hand to ask for deeper training on how to handle AI agents safely in mundane functions, it should be viewed as a professional strength, not an operational flaw.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Mark Talbot, AVP Customer Success AI Incubation at Appian. They discuss transitioning enterprise AI from isolated experiments into governed production workflows, focusing on recent research conducted in collaboration with Harvard Business Review. 

Talbot reveals a stark contrast in enterprise adoption: while 59% of organizations have AI in production, only 16% realize a high degree of measurable value. He attributes this gap to a failure to embed AI directly into core business workflows, as well as the mistake of applying AI to inefficient, broken legacy processes. 

To scale successfully, Talbot advocates for the creation of AI Centers of Excellence (CoEs) to manage data fabric, fragmentation, and strict compliance (such as SOC 2 and FedRAMP). 

Moving forward, he predicts a shift away from disconnected chatbot tools toward unified, automated platforms that offer full auditability, traceability and concrete business results.

Key Quotes

• "My lens is always where does AI fit into real work in a way that's secure, measurable, and scalable?"
• "Only sixteen percent realize a high degree of measurable value from those investments... because only eighteen percent said AI is primarily integrated into workflows."
• "If you have AI chat and you have ten thousand employees, you have ten thousand different ways of doing things. That's one of the reasons why AI needs to be embedded into existing workflows."
• "Prioritize sustainable implementation and the long term rather than chasing every AI trend."

Takeaways

• Embed AI in Workflows for True ROI: Running isolated AI experiments or simple chat windows doesn't drive top-line business growth. Organizations that embed AI directly into automated, existing workflows report significantly higher value (70% reporting moderate to substantial success) because it systematically removes human toil.
• Empower AI Centers of Excellence (CoEs): Scaling AI requires organizational discipline. Establishing an AI CoE ensures that the company maps performance metrics before and after AI deployment, maintains strict data logging, and keeps the enterprise out of the headlines for data security failures.
• Demand Traceability and Auditability: In complex, regulated environments, governance is non-negotiable. Successful deployments rely on platforms (like Appian) that provide built-in compliance frameworks (SOC 2, ISO, FedRAMP) and offer clear explainability for every decision the AI makes.
• Move Beyond Chatbots and Model Hype: The era of comparing LLMs or relying on generic chat screens is fading. The future belongs to structured platforms where the technology is invisible, secure, and seamlessly integrated into day-to-day operations to deliver scalable efficiency.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Dr. Jason Corso, Toyota Professor of AI at the University of Michigan and Co-Founder of Voxel51. They discuss Voxel51’s role as a developer tool software company for physical and visual AI, which has achieved over 4 million downloads. 

The core of the conversation focuses on Vigil, an innovative healthcare AI project led by Dr. Corso and funded by ARPA-H’s Paradigm program. Vigil tackles the critical shortage of specialists and brick-and-mortar hospitals in rural America by equipping mobile medical units (clinics on wheels) with physically grounded AI. 

Instead of replacing clinicians, Vigil acts as an advanced co-pilot, using computer vision and on-the-fly micro-guidance to upskill generalist healthcare workers (like registered nurses or EMTs) to perform complex procedures, such as cardiac ultrasound diagnostics, directly in remote communities.

Key Quotes

• "Voxel51 is indeed a dev tool software company for AI that supports the developer... in the spaces of physical AI and visual AI."
• "I don't think AI is here to replace humans... I just believe that we are as technologists in AI, we are building tools that will augment humans."
• "We have this notion of a triangle of trust where the healthcare worker is trusting Vigil to help him or her, and the patient is trusting the healthcare worker, and then tacitly, the patient is trusting Vigil."
• "In the healthcare, in the visual domain, we can't hallucinate, first of all... We're really trying to get toward those guaranteeable guardrails."

Takeaways

• Upskilling the Generalist Workforce: AI can dramatically expand healthcare access without needing to "clone specialists." By equipping existing local nurses or EMTs with AI-guided tools, they can perform specialized tasks—like capturing precise cardiac ultrasound imagery—that normally require years of dedicated training.
• The "Triangle of Trust": Successful AI deployment in healthcare relies heavily on the bedside manner and human connection. The patient trusts the clinician, the clinician trusts the AI, and the patient tacitly trusts the AI. Maintaining this human-centered relationship is crucial.
• Guaranteeable Model Guardrails: Unlike conversational LLMs that are prone to hallucination and rely on post-hoc prompt filters, critical visual AI systems in healthcare require deeply grounded, mathematical, and theoretical guardrails that prevent errors before they happen to ensure patient safety.
• Augmentation over Replacement: The future of advanced technology, including robotics (like actuated robotic arms in mobile clinics), is to augment human capabilities. AI provides an extra set of un-blinded eyes and precise micron-level assistance, allowing human workers to perform their jobs faster, better, and more equitably.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews John Edwards, SVP of Citius Healthcare Consulting at CitiusTech. They discuss the rapid acceleration of AI in the healthcare sector, shifting from simple proof-of-concepts to full-scale, operationalized enterprise solutions. 

Edwards highlights that the primary barriers to healthcare AI are not technical, but human and procedural. He notes that healthcare data is uniquely time-sensitive, and capturing the unwritten clinical context from a practitioner's head requires robust data quality and "human-in-the-loop" metrics. 

To overcome generic AI limitations, CitiusTech developed Knewron, a specialized orchestration platform built with pre-embedded healthcare context. Ultimately, Edwards argues that the success of healthcare AI relies on strict governance to filter competing priorities, comprehensive change management to overcome clinician inertia, and a deep understanding of the human workflow—such as solving doctor burnout and "pajama time"—rather than just engineering prowess.

Key Quotes

●       "While we do a lot of engineering work lately, a lot of data and AI work has been dominating what we're selling because that's what people are buying. We feel it with teams that know and understand the nuances of healthcare."

●       "The elusive return on investment only really occurs when you adopt AI... it requires you to think differently than just experimenting."

●       "The biggest mistake I see people making is automating a bad process."

●       "A perfect mousetrap that's never used won't catch any mice. You need to be able to get the human side of it engaged and excited."

Takeaways

●       Overcome Clinician Inertia: Historically, adopting tools like the stethoscope took decades because doctors trusted their traditional methods. AI faces the exact same cultural resistance. Organizations must realize that driving adoption requires shifting budgets heavily toward change management—potentially spending two dollars on adoption for every one dollar spent on the technology itself.

●       Never Automate a Bad Process: Traditional healthcare processes were designed around human limitations and legacy software. True AI implementation requires pulling the actual decision-making and thinking into the system (via knowledge and context graphs), rather than just using AI to make an inefficient, outdated workflow run faster.

●       Use Healthcare-Specific AI Foundations: General AI tools lack clinical context and require rebuilding foundations from scratch every time. Utilizing industry-specific accelerators (like CitiusTech's Knewron platform) allows organizations to safely manage time-sensitive medical data and deploy agentic workflows much faster.

●       Solve Real Workforce Friction Points: Clinicians readily embrace AI when it relieves systemic burdens like "pajama time" (the hours spent typing clinical documentation into EHRs at night). Ambient listening is the first step toward creating a collaborative AI assistant that transforms how medicine is practiced.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Amit Sharma, CEO and Founder of CData. They discuss the critical challenge of enterprise AI: securely connecting advanced AI models to proprietary enterprise data (like CRM and accounting systems). Sharma explains that while AI models have vastly improved, the real bottleneck is providing them with the right business context. 

He introduces the Model Context Protocol (MCP) as a key solution for this. The conversation also covers the shift toward Agentic AI—which demands near-perfect accuracy since there is no human in the loop—and data infrastructure, where Sharma advocates for data virtualization (leaving data where it resides, including on-premise) rather than moving everything into a massive central warehouse. 

Ultimately, he views AI as a massive enhancer of human capital that will radically accelerate business timelines.

Key Quotes

"The real power of AI is only captured when AI can actually connect to enterprise data."
"The models aren't the issue. The issue is, how do we make the data and context available to AI?"
"If you have a case for keeping data on prem, they should keep the data on prem. We in fact favor solutions like virtualization, where you can leave the data where it is..."

Takeaways

Context is King, Not Just the Model: Stop waiting for a "better model" to fix your AI problems. Recent models are already highly advanced; the actual challenge is securely feeding them your specific enterprise data and business context.

Embrace the Model Context Protocol (MCP): To effectively connect AI to business data without massive token waste, organizations should adopt MCP, which is becoming the standard for securely structuring and governing how context is brought into AI models.

Agentic AI Requires Extreme Accuracy: When moving from conversational AI to Agentic AI (where AI takes actions autonomously), the margin for error shrinks to zero. Without a human-in-the-loop to catch mistakes, data accuracy and strict agent governance become paramount.

Virtualize, Don't Centralize: You don't necessarily need to move all your data into a massive central data warehouse to use AI. Leaving data where it naturally resides (including on-premise) and using data virtualization is often more secure, compliant with data residency rules, and highly efficient.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Josh Koenig, co-founder and SVP of Marketing at Pantheon. 

They discuss how the rapid adoption of AI agents and LLMs is fundamentally changing web traffic and search behavior. As more people "ask" AI instead of searching Google, organic website traffic is dropping, but the visitors who do click through are highly "primed" and ready to act. 

Koenig advises companies against turning their websites into chatbots; instead, they should focus on AI Engine Optimization (AEO) by ensuring lightning-fast load times, properly structured content, and strong third-party reviews. 

He also warns against the flood of bland, AI-generated content ("AI slop"), emphasizing that a unique brand voice is essential to stand out. Finally, he notes that as privacy changes make tools like Google Analytics less reliable, the future of metrics lies in server-side, full-clickstream tracking.

Key Quotes

"The majority of people are no longer searching. They're asking and they're having an AI, an agent, an LLM, sort of start their research."
"Trying to beat ChatGPT or Claude at its own game on your website is probably not smart."
"Fundamentals are what matter more than ever... having your website be fast, having your content be good, having a team that can move quickly without breaking things."

Takeaways

Traffic is Down, but Intent is Up: AI answers simple queries directly, reducing overall organic website traffic and increasing cost-per-click. However, visitors who bypass the AI to reach your site are much further along in their journey and highly motivated.

Optimize for AI Crawlers: To be cited in AI overviews, your website needs three things: high speed (AI bots won't wait for slow pages), well-structured content (using Q&A formats and clear H1/H2 tags), and a strong reputation on third-party review sites.

Avoid "AI Slop": The internet is being flooded with cheap, mediocre, AI-generated content. To succeed in marketing, you must educate and entertain by starting with a strong point of view and a distinct human voice that AI cannot replicate.

The Future of Analytics: Increased privacy settings and opt-outs are making traditional tools like Google Analytics less reliable. Businesses will need to shift to server-side (clickstream) tracking to accurately measure true human engagement and track how often AI crawlers are querying their sites.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani interviews Leslie Nielsen, CISO at Mimecast. They discuss Mimecast's recently released "2026 State of Human Risk Report." 

Nielsen explains that human-centric cyberattacks are escalating annually, driven by economic uncertainty and employee fears that AI might replace their jobs, making them more susceptible to malicious recruitment or carelessness. 

A major highlight of the report is the severe risk of data exfiltration; dumping sensitive corporate data (like board presentations or financial disclosures) into unsanctioned generative AI models leaks intellectual property outside the company. 

Furthermore, Nielsen warns against the uncontrolled rise of "agentic" software that bypasses change control, creates non-human identities, and lacks proper management, effectively creating rogue employees on the network. He advises leaders to use AI to fight AI, create explicit AI acceptable use policies, and treat agents with the same accountability and management as human employees, including processes for "firing" an agent.

Key Quotes

• "We have to be using AI because it's going to take AI to fight AI."
• "Traditionally, when we thought about leaks, we thought about it being posted on a web page, but now it's kind of... death by 10,000 cuts; just kind of those slow leaks that are building up."
• "Treat [agents] just like you think about who's managing employees... somebody needs to be responsible... and also be accountable if things go wrong."
• "Bad news is good news early... The faster that it can be contained, the faster we can all work better to have a safer environment."

Takeaways

• HR and Management for Agents: Organizations must treat AI agents like human employees or contractors. Someone must be officially responsible for managing, auditing, logging, and granting specific, limited permissions to every agent. They also need defined processes for onboarded and, crucially, "firing" or disconnecting an agent if things go wrong.
• New Era of Data Leaks: "Leaks" are no longer just public website postings. Employees dumping sensitive data (board decks, financials) into unsanctioned Gen AI tools to speed up their work is a dangerous new form of intellectual property exfiltration into third-party models.
• Fighting AI with AI Speed: Business leaders must equip their security teams with AI tools to handle the rapid decision-making and alert volume required in modern defense. An AI speeds up development and increases threat vectors; human SoC analysts cannot keep up alone.
• Vigilance for Everyday Users: AI has made phishing and scam attempts extremely convincing. AI-written emails rose from 3% to 17% in late 2024/early 2025. Everyday users must pause, verify identity via an alternate known channel (like a direct phone call), and remember that if something seems too good to be true, it is.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/

In this episode of "Full Tech Ahead," host Amanda Razani speaks with Prashanth Saradesai, Head of AI for Wiss, about how accounting and finance organizations can successfully implement artificial intelligence. 

Saradesai shares insights from evaluating over 200 AI vendors, noting that many finance teams hesitate to adopt AI because they struggle to prove a clear Return on Investment (ROI). He explains that treating AI as an isolated "point solution" (e.g., merely extracting invoice data) is ineffective; instead, organizations must automate end-to-end workflows to see real value. 

Furthermore, he emphasizes that in the strict finance sector, "good enough" is unacceptable, making human-in-the-loop processes essential to mitigate AI hallucinations. 

Finally, he advises companies to refine their internal processes, build strong data foundations, and prioritize change management to ensure a successful AI adoption.

Key Quotes: 

"In finance, 'good enough' is not an answer. You can't say, 'hey, my finances are good enough.' Even one number doesn't work; it flows through your financial reporting."
"If leadership has the vision of bringing AI to the organization, but if you are not bringing everybody in the organization... there won't be any value."
"Start investing in AI from the perspective of using it for your end-to-end workflow... being AI native, thinking from a standpoint of making all your employees AI fluent, are the North Stars."

Takeaways:

Focus on End-to-End Workflows: Using AI for a single task like invoice extraction won't drive significant ROI. AI should be implemented to handle the entire workflow—from extraction and matching to approvals, posting entries, and reconciliation.

Fix Processes and Data First: The biggest mistake companies make is starting with the technology. Organizations must first clearly define their internal processes, clear bottlenecks, and build a solid, well-organized data foundation before deploying AI.

Prioritize Change Management: An AI initiative will fail if the vision stays only at the executive level. Training employees on both the advantages and the limitations (like hallucinations) of AI is crucial for successful, company-wide adoption.

Capture Decision "Context": As the industry moves toward autonomous AI agents capable of "computer use," the organizations that will succeed are those building "context graphs"—documenting not just their raw data, but the specific reasons and context behind their past business decisions.

Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/

Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/

Visit the FTA website: https://fulltechahead.com/

Check out the Substack Channel: https://fulltechahead.substack.com/