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?

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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.

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

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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.

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

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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'.

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🔧 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.

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💡 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

for - quote - AI - AI is accelerating AI research itself

for - AI - Chinese know more than US government about latest US frontier AI research

https://web.archive.org/web/20250423134653/https://www.rijksoverheid.nl/documenten/publicaties/2025/04/22/het-overheidsbrede-standpunt-voor-de-inzet-van-generatieve-ai Rijksstandpunt genAI, mede gebaseerd op IEC advies IPO. Niettemin wordt het hier lijkt me behoorlijk vrij gegeven, en de formulering klinkt heel los. Gaat problemen opleveren, want een bmw die met genAI speelt bij het opstellen van een stuk het voor zichzelf als 'experiment' labelt of 'innovatie' heeft het voor zich daarmee gerationaliseerd. Never mind dat experimenten gecontroleerde omstandigheden vergen, en innovatie een gedeelde intentie moet hebben in de org. Dit voelt heel zacht aan, staan de juiste dingen in desondanks

[[When Will the GenAI Bubble Burst]]

https://www.carbonbrief.org/explainer-how-china-is-managing-the-rising-energy-demand-from-data-centres/?utm_source=cbnewsletter&utm_medium=email&utm_term=2025-04-16&utm_campaign=Daily+Briefing+16+04+2025

The crime was misleading investors, not anyone else, which is very telling. The hype around "AI" - and actually hiring remote workers to do the job - and misleading customers/users doesn't matter.

Yet another example of "AI" being neither artificial nor intelligent.

Interesting cautionary tale about building out DCs in the styx, where energy is cheap but latency is high

Need to try this out.

Review coordinated by Life Science Editors Foundation Reviewed by: Dr. Angela Andersen, Life Science Editors Foundation & Life Science Editors Potential Conflicts of Interest: None

PUNCHLINE Evo 2 is a biological foundation model trained on 9.3 trillion DNA bases across all domains of life. It predicts the impact of genetic variation—including in noncoding and clinically relevant regions—without requiring task-specific fine-tuning. Evo 2 also generates genome-scale sequences and epigenomic architectures guided by predictive models. By interpreting its internal representations using sparse autoencoders, the model is shown to rediscover known biological features and uncover previously unannotated patterns with potential functional significance. These capabilities establish Evo 2 as a generalist model for prediction, annotation, and biological design.

BACKGROUND A foundation model is a large-scale machine learning model trained on massive and diverse datasets to learn general features that can be reused across tasks. Evo 2 is such a model for genomics: it learns from raw DNA sequence alone—across bacteria, archaea, eukaryotes, and bacteriophage—without explicit labels or training on specific tasks. This enables it to generalize to a wide range of biological questions, including predicting the effects of genetic variants, identifying regulatory elements, and generating genome-scale sequences or chromatin features.

Evo 2 comes in two versions: one with 7 billion parameters (7B) and a larger version with 40 billion parameters (40B). These numbers reflect the number of trainable weights in the model and influence its capacity to learn complex patterns. Both models were trained using a context window of up to 1 million tokens—where each token is a nucleotide—allowing the model to capture long-range dependencies across entire genomic regions.

Evo 2 learns via self-supervised learning, a method in which the model learns to predict masked or missing DNA bases in a sequence. Through this simple but powerful objective, the model discovers statistical patterns that correspond to biological structure and function, without being told what those patterns mean.

QUESTION ADDRESSED Can a large-scale foundation model trained solely on genomic sequences generalize across biological tasks—such as predicting mutational effects, modeling gene regulation, and generating realistic genomic sequences—without supervision or task-specific tuning?

SUMMARY The authors introduce Evo 2, a foundational model for genomics that generalizes across DNA, RNA, and protein tasks. Without seeing any biological labels, Evo 2 learns the sequence rules governing coding and noncoding function, predicts variant effects—including in BRCA1/2 and splicing regions—and generates full-length genomes and epigenome profiles. It also enables epigenome-aware sequence design by coupling sequence generation with predictive models of chromatin accessibility.

To probe what the model has learned internally, the authors use sparse autoencoders (SAEs)—a technique that compresses the model’s internal activations into a smaller set of interpretable features. These features often correspond to known biological elements, but importantly, some appear to capture novel, uncharacterized patterns that do not match existing annotations but are consistently associated with genomic regions of potential functional importance. This combination of rediscovery and novelty makes Evo 2 a uniquely powerful tool for exploring both the known and the unknown genome.

KEY RESULTS Evo 2 trains on vast genomic data using a novel architecture to handle long DNA sequences Figures 1 + S1 Goal: Build a model capable of representing entire genomic regions (up to 1 million bases) from any organism. Outcome: Evo 2 was trained on 9.3 trillion bases using a hybrid convolution-attention architecture (StripedHyena 2). The model achieves long-context recall and strong perplexity scaling with increasing sequence length and model size.

Evo 2 predicts the impact of mutations across DNA, RNA, and protein fitness Figures 2A–J + S2–S3 Goal: Assess whether Evo 2 can identify deleterious mutations without supervision across diverse organisms and molecules. Outcome: Evo 2 assigns lower likelihoods to biologically disruptive mutations—e.g., frameshifts, premature stops, and non-synonymous changes—mirroring evolutionary constraint. Predictions correlate with deep mutational scanning data and gene essentiality assays. Evo 2 embeddings also support highly accurate exon-intron classifiers.

Clarification: “Generalist performance across DNA, RNA, and protein tasks” means that Evo 2 can simultaneously make accurate predictions about the functional impact of genetic variants on transcription, splicing, RNA stability, translation, and protein structure—without being specifically trained on any of these tasks.

Evo 2 achieves state-of-the-art performance in clinical variant effect prediction Figures 3A–I + S4 Goal: Evaluate Evo 2's ability to predict pathogenicity of human genetic variants. Outcome: Evo 2 matches or outperforms specialized models on coding, noncoding, splicing, and indel variants. It accurately classifies BRCA1/2 mutations and generalizes to novel variant types. When paired with supervised classifiers using its embeddings, it achieves state-of-the-art accuracy on BRCA1 variant interpretation.

Evo 2 representations reveal both known and novel biological features through sparse autoencoders Figures 4A–G + S5–S7 Goal: Understand what Evo 2 has learned internally. Outcome: Sparse autoencoders decompose Evo 2’s internal representations into distinct features—many of which align with well-known biological elements such as exon-intron boundaries, transcription factor motifs, protein secondary structure, CRISPR spacers, and mobile elements. Importantly, a subset of features do not correspond to any known annotations, yet appear repeatedly in biologically plausible contexts. These unannotated features may represent novel regulatory sequences, structural motifs, or other functional elements that remain to be characterized experimentally.

Note: Sparse autoencoders are neural networks that reduce high-dimensional representations to a smaller set of features, enforcing sparsity so that each feature ideally captures a distinct biological signal. This approach enables mechanistic insight into what the model “knows” about sequence biology.

Evo 2 generates genome-scale sequences with realistic structure and content Figures 5A–L + S8 Goal: Assess whether Evo 2 can generate complete genome sequences that resemble natural ones. Outcome: Evo 2 successfully generates mitochondrial genomes, minimal bacterial genomes, and yeast chromosomes. These sequences contain realistic coding regions, tRNAs, promoters, and structural features. Predicted proteins fold correctly and recapitulate functional domains.

Evo 2 enables design of DNA with targeted epigenomic features Figures 6A–G + S9 Goal: Use Evo 2 to generate DNA sequences with user-defined chromatin accessibility profiles. Outcome: By coupling Evo 2 with predictors like Enformer and Borzoi, the authors guide generation to match desired ATAC-seq profiles. Using a beam search strategy—where the model explores and ranks multiple possible output sequences—it generates synthetic DNA that encodes specific chromatin accessibility patterns, such as writing “EVO2” in open/closed chromatin space.

STRENGTHS First large-scale, open-source biological foundation model trained across all domains of life

Performs well across variant effect prediction, genome annotation, and generative biology

Demonstrates mechanistic interpretability via sparse autoencoders

Learns both known and novel biological features directly from raw sequence

Unsupervised learning generalizes to clinical and functional genomics

Robust evaluation across species, sequence types, and biological scales

FUTURE WORK & EXPERIMENTAL DIRECTIONS Expand training to include viruses that infect eukaryotic hosts: Evo 2 currently excludes these sequences, in part to reduce potential for misuse and due to their unusual nucleotide structure and compact coding. As a result, Evo 2 performs poorly on eukaryotic viral sequence prediction and generation. Including these genomes could expand its applications in virology and public health.

Empirical validation of novel features: Use CRISPR perturbation, reporter assays, or conservation analysis to test Evo 2-derived features that don’t align with existing annotations.

Targeted mutagenesis: Use Evo 2 to identify high-impact or compensatory variants in disease-linked loci, and validate using genome editing or saturation mutagenesis.

Epigenomic editing: Validate Evo 2-designed sequences for chromatin accessibility using ATAC-seq or synthetic enhancer assays.

Clinical applications: Fine-tune Evo 2 embeddings to improve rare disease variant interpretation or personalized genome annotation.

Synthetic evolution: Explore whether Evo 2 can generate synthetic genomes with tunable ecological or evolutionary features, enabling testing of evolutionary hypotheses.

AUTHORSHIP NOTE This review was drafted with support from ChatGPT (OpenAI) to help organize and articulate key ideas clearly and concisely. I provided detailed prompts, interpretations, and edits to ensure the review reflects an expert understanding of the biology and the paper’s contributions. The final version has been reviewed and approved by me.

FINAL TAKEAWAY Evo 2 is a breakthrough in foundation models for biology—offering accurate prediction, functional annotation, and genome-scale generation, all learned from raw DNA sequence. By capturing universal patterns across life, and identifying both well-characterized and unknown sequence features, Evo 2 opens powerful new directions in evolutionary biology, genomics, and biological design. Its open release invites widespread use and innovation across the life sciences.

In our paper, we address this idea that ALL ✨sparkling intelligence✨ outputs are generated using the same technology and practices. We argue that it is useful to have a term for those outputs that don't match our shared reality or factual requirements, and for that we propose "mirage".

In my blog post introducing our paper, I suggest AI "rainbows" as a term for mirages that we do value.

I'm assuming this is some kind of ✨sparkling intelligence✨ and given that Dr. Oblivion seems to miss the point of the paper and our discussion here, I found it more illustrative than helpful ;)

Turns out the story is a bit more complicated than that, at least according to the history shared by another participant below.

This is a great history of the term "hallucination" in the discourse of ✨sparkling intelligence✨ — huge thanks to whoever shared it! I've also added it to our collaborative bibliography.

deepseek推理型 #AI #大模型