Include any metrics on user engagement or feedback received from testing the game.

What was the outcome of this exploration? Did it lead to a successful game or user feedback?

Highlight any improvements in uptime or user satisfaction as a result of your maintenance efforts.

Quantify the number of issues resolved or average response time to demonstrate effectiveness.

Make this more action-oriented. Specify how many computers were set up or any efficiency gains.

Clarify outcomes of this exploration. Did it lead to any implemented solutions or improvements?

Add metrics on user engagement or performance improvements achieved through this application.

Specify the impact of the API. Did it improve efficiency or user experience? Quantify if possible.

Specify the benefits: what improvements in performance or reliability did this deployment achieve?

Quantify success: how many tasks were managed or users gained efficiency through this app?

Clarify the impact: how many users utilized this app, and what problems did it solve for them?

Include metrics: how many users tested it, and what feedback did you receive on gameplay?

Add specifics: what was the goal of this exploration, and how was it received by users or testers?

Quantify your impact: how many issues resolved per week/month and any improvements in satisfaction?

Make this more impactful: how many computers were set up, and what was the result of this setup?

Clarify the outcome: did this lead to improved collaboration or understanding among students?

Quantify the results: what insights were gained or how many users benefited from this app?

Specify the impact: how did this API improve data handling or user experience?

Explain the benefits of this deployment choice, such as improved load times or scalability.

Include user statistics or feedback to illustrate the app's effectiveness and user satisfaction.

Avoid redundancy; clarify unique features or user benefits that distinguish this app from others.

Mention any feedback received from users or improvements in gameplay as a result of your development.

Include the average resolution time or percentage of issues resolved to show effectiveness.

Quantify the number of computers set up or the efficiency gained through your communication efforts.

Clarify the outcome of this exploration; did it lead to any implemented solutions or insights?

Add metrics on user engagement or performance improvements from the app to demonstrate impact.

Specify how the API improved data management or user experience to quantify impact.

Mention any cost savings or performance enhancements achieved through this deployment strategy.

Specify the performance improvement, such as reduced query time or increased data retrieval speed.

Quantify user engagement or efficiency improvements, e.g., 'reduced task completion time by X%'.

Remove 'using with' for clarity. Add impact metrics, such as user adoption rates or performance improvements.

Mention any user feedback or performance metrics that demonstrate the game's success or engagement.

Include metrics on the number of issues resolved or average resolution time to show efficiency.

Quantify the number of computers set up or the time saved through your communication efforts.

Clarify the outcome of this exploration. Did it lead to any implemented solutions or improved learning outcomes?

Add metrics on user engagement or efficiency improvements resulting from the application.

Specify the impact of the API, e.g., improved data retrieval speed by X% or reduced storage costs by Y%.

Clarify the potential applications or benefits of this technology to provide context for its significance.

Specify the expected impact of this project. How will it benefit users or improve internship accessibility?

Provide context on what 'efficiency' means here. What specific tasks were made easier or faster?

Specify how this automation improved deployment frequency or reduced errors in production.

Clarify the baseline metrics for latency and cost to provide context for the improvements made.

Quantify the efficiency gained or time saved through this consolidation to better illustrate the impact.

Explain how the $240,000 savings was calculated and what specific processes were improved to achieve this.

Break down the specific operational problems solved and how they directly impacted user experience or system reliability.

Quantify the reduction in phishing incidents or security breaches to highlight the effectiveness of your solutions.

Clarify how this paradigm shift benefited AWS users or reduced costs. Provide measurable outcomes.

Specify the impact of maintaining these services. How did it improve customer experience or system performance?

Specify the impact of alerts (e.g., 'reducing response time to market changes by 50%').

Clarify the original hosting cost for context (e.g., 'from $200/month to $20/month').

Quantify the reduction in clutter (e.g., 'reducing review threads by 50%').

Be specific about the improvements (e.g., 'reducing review time by 30%').

Specify the impact on user experience (e.g., 'increasing mobile user engagement by 20%').

Quantify the efficiency gained (e.g., 'reducing data retrieval time by 40%').

Clarify the benefit to executives (e.g., 'improving decision-making speed').

Quantify the time reduction (e.g., 'reducing setup time by 50%') for stronger impact.

Specify the original coverage percentage for context (e.g., 'from 60% to 75%').

Quantify the time saved (e.g., 'saving 10+ hours monthly') for clearer impact.

Quantify the number of users or sessions to demonstrate the app's reach and impact.

Mention any user engagement metrics or feedback to showcase the bot's effectiveness.

Clarify the outcome of these alerts; how did it benefit the DAOs or crypto funds?

Include the original hosting cost to emphasize the financial impact of your re-architecture.

Specify the improvements made or metrics that show the effectiveness of the bot.

Provide specific user feedback or metrics that demonstrate the improvement in user experience.

Clarify the impact on decision-making or efficiency for the executives who used the dashboards.

Quantify the time saved in testing setup to illustrate the impact of your tool.

Specify the initial coverage percentage to highlight the improvement more effectively.

Quantify the exact hours saved to provide a clearer impact of your work.

Quantify user engagement or improvements in study habits resulting from the application.

Include user metrics or feedback to demonstrate the tool's adoption and effectiveness.

Specify the frequency or significance of alerts triggered to highlight the system's effectiveness.

Clarify how cost savings were redirected or reinvested, or the impact on project scalability.

Quantify the reduction in time spent on reviews or the increase in review efficiency.

Provide specific outcomes from the feedback integration, such as user adoption rates or satisfaction scores.

Add metrics on user engagement or satisfaction improvements post-redesign.

Quantify the impact of this access on decision-making or operational efficiency for the executives.

Explain how this time savings affected project timelines or team productivity.

Specify the impact of increased coverage on the project's reliability or performance metrics.

Clarify how this automation improved HR efficiency or employee satisfaction beyond time savings.

Add metrics on user engagement or feedback to showcase the app's impact on student productivity.

Quantify user adoption or transaction volume to illustrate the tool's success.

Specify the frequency of alerts or actions taken to demonstrate the system's effectiveness.

Clarify the previous hosting costs to emphasize the financial impact of this change.

Include metrics on how many resumes were improved or the feedback received from users.

Highlight any specific contributions or outcomes from these meetings to show leadership or initiative.

Add metrics on user engagement or satisfaction improvements post-redesign.

Quantify the time saved for executives or any decision-making improvements resulting from this change.

Explain how this time savings affected project timelines or team productivity.

Specify the original coverage percentage to better illustrate the improvement's significance.

Clarify how this automation impacted overall HR efficiency or employee satisfaction.

Consider shortening for clarity; e.g., 'Developing an AI agent to monitor stablecoin flows and trigger alerts for large movements.'

Break into two sentences for clarity; consider rephrasing 'automated change detection' to 'automated detection of changes'.

Specify 'driving fast and iterative improvements' with measurable outcomes, e.g., 'resulting in 30% faster review times'.

Use active voice: 'Collaborated in daily scrum meetings with a team of 5 developers...' for a stronger impact.

Quantify 'critical responsiveness issues' with specifics to enhance impact; e.g., 'fixed 5 critical responsiveness issues'.

Consider rephrasing 'eliminating the need for 100+ complex spreadsheets' to 'replacing 100+ complex spreadsheets' for stronger impact.

Rephrase to 'reducing developer testing setup time by 86% by eliminating test accounts' for conciseness.

Break into two sentences for clarity; consider rephrasing 'increasing overall coverage by 15%' to 'increasing test coverage by 15%'.

Clarify '50+ hr/month' to '50+ hours/month' for grammatical accuracy.

Add a space after the pipe for better readability: 'Jazz Aviation | React, TypeScript...'.

Use 'May 2025 – Present' for consistency with other dates. Avoid periods after 'May' and 'Present'.

Specify user engagement metrics or feedback to illustrate the bot's effectiveness and popularity.

Clarify the potential impact or use cases of this AI agent to highlight its significance.

Include specific performance metrics or improvements achieved post-rearchitecture for clarity.

Add specific metrics on how many resumes were improved or how quickly to demonstrate impact.

Focus on your contributions or outcomes from these meetings to highlight your role more effectively.

Clarify how this change improved decision-making or efficiency for the executives.

Quantify the time saved in hours or days to provide clearer context for the impact.

Specify the business impact of the feature beyond time savings to enhance significance.

Quantify the number of users or sessions tracked to demonstrate the app's usage and impact.

Include user adoption rates or feedback to illustrate the bot's effectiveness and popularity.

Clarify the potential financial impact or risk reduction achieved through this AI agent's alerts.

Add metrics on how many resumes were improved or user satisfaction ratings to demonstrate impact.

Highlight a specific contribution or idea that led to a significant improvement in team performance.

Specify the user engagement metrics or feedback received post-redesign to showcase impact.

Quantify the decision-making improvements or time saved for executives due to the dashboards.

Consider rephrasing to emphasize how this tool improved support response times or user experience.

Add a specific example of a critical bug found to highlight the importance of your contributions.

Clarify the impact by stating how this improved efficiency or employee satisfaction beyond just time saved.

Highlight any specific contributions or outcomes from these meetings to showcase teamwork impact.

Add user metrics or feedback to demonstrate the app's effectiveness or popularity.

Explain the significance of these alerts—how they improve decision-making or risk management.

Provide the original hosting cost for a clearer picture of the savings achieved.

Mention specific metrics, like the number of resumes improved or user engagement rates.

Be more specific about the user experience improvements or feedback received post-redesign.

Quantify the time or efficiency gained by eliminating these spreadsheets to highlight the impact.

Include the previous setup time for context; this enhances the impact of the achievement.

Clarify what 'coverage' refers to—unit tests, code coverage, etc.—for better understanding.

Specify the impact of time saved on overall productivity or cost savings for the company.

Mention any user adoption rates or feedback to highlight the application's success and relevance.

Consider adding user engagement metrics or feedback to demonstrate the bot's effectiveness.

Clarify the potential impact of this AI agent on decision-making or risk management for stakeholders.

Consider specifying the previous hosting costs to give a clearer picture of the savings achieved.

Include metrics on how many resumes were improved or the average improvement rate to show effectiveness.

Highlight any specific contributions or outcomes from these meetings to demonstrate leadership.

Use active verbs like 'improved' or 'enhanced' to convey a stronger impact.

Quantify the time saved for executives to highlight the efficiency gained through your work.

Clarify how this time saving directly benefits project timelines or resource allocation.

Specify the baseline coverage percentage to give context to the improvement achieved.

Consider rephrasing to emphasize the impact of the automation on employee productivity and morale.

🔧 Mention any user feedback or metrics that demonstrate the app's success.

🔧 Specify how this bot improved user experience or efficiency.

🔧 Clarify the potential business impact or use case for this AI agent.

📝 Remove extra space before the comma after PHP and ensure consistent spacing throughout.

💪 Great initiative; consider mentioning any user engagement metrics to showcase success.

💡 Include user metrics or feedback to demonstrate the app's effectiveness or popularity.

🔧 Specify the impact of dashboards on decision-making or efficiency to enhance this bullet.

💪 Comprehensive skill set; consider prioritizing skills relevant to the job you are applying for.

🔧 Consider adding the date of certification to provide context on your qualifications.

📝 Add spaces after commas for consistency in project formatting (e.g., AWS (Lambda, DynamoDB, S3)).

💡 Consider highlighting specific contributions or outcomes from scrum participation for added impact.

💪 Excellent use of metrics to showcase efficiency gains; maintain this focus on results.

💪 Quantified achievement effectively demonstrates impact; continue this practice throughout.

💪 Strong GPA reflects academic excellence; consider adding relevant coursework to highlight skills.

🔧 Add specific technologies used or methodologies applied to enhance technical depth.

💪 Unique project showcases initiative and technical skills; ensure all projects highlight similar impact.

💪 Certification adds credibility; consider adding more certifications or relevant training if available.

💡 Consider listing courses relevant to desired job roles to enhance alignment with job descriptions.

📝 Remove extra spaces and ensure consistent punctuation for a cleaner look.

🔧 Clarify the impact of this feature on user experience or efficiency for better context.

💡 Consider emphasizing specific contributions or outcomes from scrum meetings to highlight teamwork.

💪 Excellent use of metrics to illustrate efficiency improvements; maintain this level of detail.

💪 Quantified achievement effectively showcases impact; continue this trend in all experience descriptions.

💪 Strong GPA demonstrates academic excellence; consider adding relevant coursework to highlight skills.

Comparisons of text versus image versus video generation

https://web.archive.org/web/20250719082633/https://paulkedrosky.com/honey-ai-capex-ate-the-economy/ by Paul Kedrosky On the enormous capital expenditures for AI related data centers, at the level of 2% US GDP. A volume big enough to influence overall economy / hide other currents in economy.

Summary: Navigating Failures in Pods With Devices

This article examines the unique challenges Kubernetes faces in managing specialized hardware (e.g., GPUs, accelerators) within AI/ML workloads, and explores current pain points, DIY solutions, and the future roadmap for more robust device failure handling.

Why AI/ML Workloads Are Different

• Heavy Dependence on Specialized Hardware: AI/ML jobs require devices like GPUs, with hardware failures causing significant disruptions.
• Complex Scheduling: Tasks may consume entire machines or need coordinated scheduling across nodes due to device interconnects.
• High Running Costs: Specialized nodes are expensive; idle time is wasteful.
• Non-Traditional Failure Models: Standard Kubernetes assumptions (like treating nodes as fungible, or pods as easily replaceable) don’t apply well; failures can trigger large-scale restarts or job aborts.

Major Failure Modes in Kubernetes With Devices

1. Kubernetes Infrastructure Failures

   • Multiple actors (device plugin, kubelet, scheduler) must work together; failures can occur at any stage.
   • Issues include pods failing admission, poor scheduling, or pods unable to run despite healthy hardware.
   • Best Practices: Early restarts, close monitoring, canary deployments, use of verified device plugins and drivers.

2. Device Failures

   • Kubernetes has limited built-in ability to handle device failures—unhealthy devices simply reduce the allocatable count.
   • Lacks correlation between device failure and pod/container failure.
   • DIY Solutions:
     • Node Health Controllers: Restart nodes if device capacity drops, but these can be slow and blunt.
     • Pod Failure Policies: Pods exit with special codes for device errors, but support is limited and mostly for batch jobs.
     • Custom Pod Watchers: Scripts or controllers watch pod/device status, forcibly delete pods attached to failed devices, prompting rescheduling.

3. Container Code Failures

   • Kubernetes can only restart containers or reschedule pods, with limited expressiveness about what counts as failure.
   • For large AI/ML jobs: Orchestration wrappers restart failed main executables, aiming to avoid expensive full job restart cycles.

4. Device Degradation

   • Not all device issues result in outright failure; degraded performance now occurs more frequently (e.g., one slow GPU dragging down training).
   • Detection and remediation are largely DIY; Kubernetes does not yet natively express "degraded" status.

Current Workarounds & Limitations

• Most device-failure strategies are manual or require high privileges.
• Workarounds are often fragile, costly, or disruptive.
• Kubernetes lacks standardized abstractions for device health and device importance at pod or cluster level.

Roadmap: What’s Next for Kubernetes

SIG Node and Kubernetes community are focusing on:

• Improving core reliability: Ensuring kubelet, device manager, and plugins handle failures gracefully.
• Making Failure Signals Visible: Initiatives like KEP 4680 aim to expose device health at pod status level.
• Integration With Pod Failure Policies: Plans to recognize device failures as first-class events for triggering recovery.
• Pod Descheduling: Enabling pods to be rescheduled off failed/unhealthy devices, even with restartPolicy: Always.
• Better Handling for Large-Scale AI/ML Workloads: More granular recovery, fast in-place restarts, state snapshotting.
• Device Degradation Signals: Early discussions on tracking performance degradation, but no mature standard yet.

Key Takeaway

Kubernetes remains the platform of choice for AI/ML, but device- and hardware-aware failure handling is still evolving. Most robust solutions are still "DIY," but community and upstream investment is underway to standardize and automate recovery and resilience for workloads depending on specialized hardware.

C=EP-V Cheating equation and AI discussion

What is an agent? read more in detail

A Harvard Law graduate who argued before the Supreme Court fed his case briefs into Claude 4 Opus and had it answer the same questions the Justices posed to him. The AI delivered what he called an "outstanding oral argument" with coherent answers and clever responses he hadn't considered, leading him to conclude that AI lawyers could soon outperform even top human advocates at oral argument.

Oh, so to get the highest throughout, the inference servers also batch operations making it look a bit like training too

https://web.archive.org/web/20250630134724/https://www.theregister.com/2025/06/29/ai_agents_fail_a_lot/

'agent washing' Agentic AI underperforms, getting at most 30% tasks right (Gemini 2.5-Pro) but mostly under 10%.

Article contains examples of what I think we should agentic hallucination, where not finding a solution, it takes steps to alter reality to fit the solution (e.g. renaming a user so it was the right user to send a message to, as the right user could not be found). Meredith Witthaker is mentioned, but from her statement I saw a key element is missing: most of that access will be in clear text, as models can't do encryption. Meaning not just the model, but the fact of access existing is a major vulnerability.

LOL, do people really use GPU's without any surrounding infrastructure?

for - post LinkedIn - book - From Bacteria to AI - reminds me of Micheal Levin's cognitive light cones - adjacency - Micheal Levin's - Katherine Hayle - cognition

for - progress trap - AI - sharing intimate details with

Pulling back to this post - https://wiobyrne.com/embracing-the-violin-of-cognitive-amplification/

https://web.archive.org/web/20250603172833/https://www.wsj.com/articles/ai-startup-boom-raises-questions-of-exaggerated-tech-savvy-11565775004

2019 WSJ report on the vaporware Build.ai. Folded 6 yrs later. Meaning investors turned a blind eye for all that time.

https://web.archive.org/web/20250603172659/https://techcrunch.com/2022/03/30/builder-ai-raises-100m-series-c-led-by-insight-partners-to-scale-up-its-software-automation/

March 2022 Techcrunch article already pointing to the vapor aspect of Builder.ai which folded in June 2025. Points back to 2019 reports where the same issue was already raised. Meaning later investors were wilfully blind for 6 yrs.

'AI' softw dev company, is actually a pool of 700 India based coders. Exposed because they couldn't meet payroll....

• NVIDIA’s latest earnings highlight a dramatic surge in AI demand, driven by a shift from simple one-shot inference to more complex, compute-intensive reasoning tasks.
• Reasoning models require hundreds to thousands of times more computational resources and tokens per task, significantly increasing GPU usage, especially for AI coding agents and advanced applications.
• Major hyperscalers like Microsoft, Google, and OpenAI are experiencing exponential growth in token generation, with Microsoft alone processing over 100 trillion tokens in Q1—a fivefold year-over-year increase.
• Hyperscalers are deploying nearly 1,000 NVL72 racks (72,000 Blackwell GPUs) per week, and NVIDIA-powered “AI factories” have doubled year-over-year to nearly 100, with the average GPU count per factory also doubling.
• To meet this unprecedented demand, more than $300 billion in capital expenditure is being invested this year in data centers (rebranded by NVIDIA as “AI factories”), signaling a new industrial revolution in AI infrastructure.

wish there was a clear cut definition or at least advertisement of authors' stakes, stances, and definitions of the following terms

technological determinism; agent; intelligence; control; progress; alignment

for - question - progress trap - open source AI models - for blackmail and ransom - Could a bad actor take an open source codebase and twist it to do harm like find out about an rogue AI creator's adversary, enemy or victim and blackmail them? - progress trap - open source AI - criminals - exploit to identify and blackmail victiims

for - progress trap - AI - Anthropic Claude 4 - blackmail - from - youtube - Kyle Kilinski Show - AI is completely out of control - https://hyp.is/GhDOzj0nEfCvHZdiUaw4gQ/www.youtube.com/watch?v=4j1gjSoRt8Q

for - progress trap - AI - Anthropic Claude 4 - blackmail - rare behavior - but still possible! It only has to happen once!

for - progress trap - AI - blackmail - AI - autonomy - progress trap - AI - Anthropic - Claude Opus 4 - to - article - Anthropic Claude 4 blackmail and news leak - progress trap - AI - article - Anthropic Claude 4 - blackmail - rare behavior - Anthropic’s new AI model didn’t just “blackmail” researchers in tests — it tried to leak information to news outlets

for - progress trap - AI - AI - blackmailing human creators - AI - autonomy

for - progress trap - AI - autonomy - example - Grok

New stat from IBM? This is similar to the RAND figure from before?

for - progress trap - AI - Grok - Elon Musk programs Grok to lie about South African refuges

https://www.derstandard.at/story/3000000268663/ich-kann-nicht-atmen-musks-supercomputer-nimmt-den-menschen-in-memphis-die-luft

for - natural language acquisition - Automatic Language Growth - ALG - youtube - interview - David Long - Automatic Language Growth - from - youtube - The Language School that Teaches Adults like Babies - https://hyp.is/Ls_IbCpbEfCEqEfjBlJ8hw/www.youtube.com/watch?v=984rkMbvp-w

summary - The key takeaway is that even as adults, we have retained our innate language learning skill which requires simply treating a new language as a new, novel experience that we can apprehend naturally simply by experiencing it like the way we did when we were exposed to our first, native language - We didn't know what a "language" was theoretically when we were infants, but we simply fell into the experience and played with the experiences and our primary caretakers guided us - We didn't know grammar and rules of language, we just learned innately

is this not how CAPTCHA is evaluated too?

cf./viz. classical statistical machine learning and language models

anatomy of a landscape / atrocity exhibition

for - report - America's Superintelligence Project - definition - ASI - Artificial Super Intelligence

summary - What is the cost of mistrust between nation states? - The mistrust between the US and China is reaching an all-tie high and it has disastrous consequences for an AI arms race - It is driving each country to move fast and break things, which will become an existential threat to all humanity - Deep Humanity, with an important dimension of progress traps can help us navigate ASI

for - definition - AI containment - progress trap - AI containment

for - example - AI unpredicted behavior

for - high security risk - US AI labs