AI Deep Dives & Tutorials

Detailed breakdowns, step-by-step guides, and video demos that show how to create content with AI and where to apply new tools.

171 articles • Page 3 of 12

Jun 22, 2026

LLMs degrade after 15 turns; new industry tactics emerge

Studies suggest that language models often lose reliability after about 15-20 back-and-forths in a conversation. This may happen because the models must split their attention as the chat gets longer, making it harder to remember or follow earlier instructions. Common problems include forgetting rules, repeating answers, or making up new ones. Researchers and industry teams now use tactics like summarizing conversation history early, breaking tasks into smaller parts, and storing important facts outside the chat to help fight these issues. There is still debate about whether bigger context windows can fix the problem, but most agree that better prompt handling and context management work better than just making context windows larger.

Jun 22, 2026

Microsoft's Nadella defines AI's next battleground: the Learning Loop

Satya Nadella, Microsoft's CEO, suggests that the next big step for AI is creating a "Learning Loop" that combines human judgment with a company's own AI tools. This loop takes in decisions, results, and details from real work, then uses that data to teach private AI models, so future tasks get better over time. Examples from companies like Valeo and Toyota show that using these loops may already help save time and improve processes. Experts warn that without these learning systems, companies might face high costs and lose control if they only use outside AI tools. Building and owning this loop might give companies an advantage that others cannot easily copy.

Jun 22, 2026

Engdahl's 2028 AI prediction: What it actually means for companies

Overall, there may be more pressure on companies to invest in AI and adapt, or they could fall behind by 2028.

Jun 22, 2026

Cognite Co-founder Details Why Industrial AI Projects Fail

Many industrial AI projects do not succeed after early pilot tests. Geir Engdahl from Cognite suggests the main problems are messy data, slow integration, and a lack of trusted systems for scaling up. He says success may depend on clear rules, good data, and operator trust, not just better algorithms. New tools like knowledge graphs appear to help by making data easier to understand and audit. Some experts believe that by 2028, companies without these systems may fall behind, as using AI for operations could lead to higher profits.

Jun 22, 2026

Cognite, NVIDIA integrate AI for industrial predictive operations

Cognite and NVIDIA have combined their technologies to improve predictive operations in industrial settings. Time-series models can detect unusual patterns, but knowledge graphs add important context by linking sensor data to asset relationships and business impact. The integration at Celanese's Texas facility reportedly helped move from manual checks to predictive operations, which may have improved efficiency. Data integration remains challenging because information is stored in many different systems. Experts suggest that success should be measured by business outcomes like reduced downtime, not just technical model metrics.

Jun 17, 2026

Veracode: 45% of AI-Generated Code Fails Security Tests

Around 45% of AI-generated code may fail key security tests, according to Veracode's 2025 report. Some suggested code dependencies might not actually exist, which appears to raise supply-chain risks. Experts suggest mixing automated scans with human reviews, especially for sensitive areas like payments and identity. Gradual rollouts and instant rollback plans are recommended to catch problems before they spread. Measuring how often problems escape review may help teams improve both AI prompts and reviewer training.

Jun 16, 2026

Faros AI, Weave advise 4 key metrics for agent-led code

Faros AI and Weave suggest that to measure the value of agent-led code, teams should focus on four types of metrics: adoption and usage, delivery flow, quality and stability, and cost and ROI. Adoption rates may matter most until a baseline is reached, then flow and quality become more important. Time-to-merge and review speed may reveal bottlenecks better than just counting pull requests. Quality metrics like change failure rate and mean time to recovery can show hidden problems, while cost metrics may help link code changes to business impact. The guidance suggests starting with data teams already have and warns that some metrics may look good without showing real improvements.

Jun 16, 2026

Teams adopt 5-stage workflow for AI agent product catalogs

Teams building AI-ready product catalogs seem to follow a five-step workflow: ingesting data, modeling it with standard formats, enriching it with AI, validating it, and then publishing it. Using structured data like JSON-LD and live APIs may help AI shopping assistants better understand products. AI models might extract product details and write summaries, but human editors are still needed because errors or unclear results can happen. Key measures for success include how much of the catalog passes AI checks, how often searches fail, and how quickly updates appear. Some teams also add safeguards and rollback tools to fix mistakes fast and prevent bad data from spreading.

Jun 16, 2026

AI tools cut pull request review times to under 24 hours

Recent benchmarks suggest that using AI tools may help keep pull request (PR) review times under 24 hours, which appears to be linked to higher contributor engagement. A hybrid workflow is recommended, where AI scans PRs for issues and maintainers make the final decisions. Queue-based bots like GitHub Actions and Mergify can help manage many PRs by merging them one at a time after checks pass. Tracking key metrics such as time to first review and percentage of PRs merged within 24 hours may help teams spot problems early. Projects that use these practices reportedly see fewer merge conflicts and less manual work for maintainers.

Jun 13, 2026

How algorithms evolved from 9th-century math to shape today's AI

Algorithms began with 9th-century math from al-Khwārizmī, whose name became the word "algorithm." Over time, the meaning widened from simple arithmetic to any step-by-step rules, especially in computer science. Today, algorithms may shape what people see online, sometimes leading to bias or echo chambers. Studies suggest that small changes in how content is ranked might affect what news or media people consume. The history of algorithms shows their rules have always helped organize how we find and use information.

Jun 12, 2026

TechEmpower 2026 Guide Updates AI Agent Best Practices

The 2026 TechEmpower guide suggests best practices for using AI agents in production are moving from model tuning toward building strong system-level engineering. Teams that treat AI agents as important, distributed systems may see fewer problems and faster fixes. The guide recommends separating planning and execution, using strict safety checks, and always tracing actions. Reliable testing, risk checks, and human approvals for critical steps are highlighted. The sources also note that keeping observability strong and regularly testing for failures might help reduce incidents and improve user satisfaction.

Jun 11, 2026

MINT.ai and Uplane detail AI operational layer for marketing

MINT.ai and Uplane describe a framework for using AI in marketing that may help teams move from small pilot projects to everyday operations. Case studies suggest that having creative management, ad serving, and measurement on one data foundation might lead to better and faster results. The approach includes five layers: collecting data, unifying customer IDs, orchestrating workflows, AI-driven decisions, and activating plus measuring campaigns. Experts say a phase-based rollout - starting with assessment and a small pilot - could help teams scale up safely. Good governance and privacy controls appear to be important, and some reports suggest integration depth, not just the number of tools, determines success.

Jun 11, 2026

AI Compute Deals Pressure Margins, Shift Valuation Metrics

AI compute costs are rising as companies spend more on both training and using AI models. While training big models like GPT-4 may cost over $100 million, most of the long-term spending now comes from using these models (inference), which might make margins shrink if revenue growth does not keep up. Large deals to lock in chip supply can help manage risks but also create high fixed costs and reduce flexibility if demand drops or new hardware appears. Some reports suggest that missing usage targets by even a small amount may erase profits. Overall, how well these companies match their compute supply with actual usage may decide if they stay profitable.

Jun 10, 2026

AI chip deals convert variable spend into fixed cost for buyers

AI chip deals may turn what used to be unpredictable, variable spending into a fixed cost for buyers. Even though the unit price for running AI models (inference) has dropped sharply, total spending keeps rising because use is growing even faster. Some sources suggest that inference now accounts for most of the lifetime cost of running AI, and using chips efficiently becomes crucial for profits. Long-term chip contracts might help buyers by securing supply but can also create financial risks if actual usage falls short. The overall impact on companies depends on whether they can keep their chip usage high enough to justify the fixed costs.

Jun 7, 2026

Microsoft Research unveils new methods to boost LLM reasoning

Microsoft Research has introduced new methods that may improve reasoning in language models by adding structured logic on top of their pattern recognition abilities. Studies suggest that combining language models with external symbolic solvers can raise accuracy, especially on math tasks, but may still leave some reliability gaps. Researchers now focus on using statistical tools to monitor and evaluate models for issues like drift or safety problems. Teams often use a set of simple metrics and regular tests to catch failures early. Experts believe that treating language models as statistical tools first, while adding clear logic checks, might lead to more reliable and safer AI systems.