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Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI

Judy — Sat, 03 Oct 2026 01:00:28 +0000

This article is a deep-dive from JudyAI Lab — an AI engineering playbook series with 100+ published guides, 5,000+ weekly readers across 60+ countries, focused on the practical side of running AI agents, trading systems, and content pipelines in production.

📰 Key Takeaways

NVIDIA has released Nemotron 3.5 Content Safety, a multimodal safety classifier built for enterprise AI applications, based on Google Gemma 3 4B with LoRA fine-tuning and deployable with just 8GB+ VRAM. The biggest difference from the previous generation is "unified multimodal evaluation": a single inference pass handles the user prompt, image, and assistant response together, catching violation risk that emerges from the interaction between text and image, without needing separate scoring passes. On language coverage, the model is explicitly trained on 12 languages (including Chinese, English, Japanese, Korean, and Arabic), and zero-shot generalization from the Gemma 3 base extends that to roughly 140 languages. 99% of the training data comes from real photos, deliberately avoiding the common SDXL synthetic-image approach to stay close to production conditions. The model offers three output modes: a plain binary verdict, a verdict plus safety category, and a THINK mode that outputs a step-by-step reasoning trace — typically just 2-3 sentences — with latency overhead under a third of alternative approaches and token usage cut by up to 50%. Enterprises can inject custom policy descriptions at inference time, supporting suppression of specific categories or addition of industry-specific risk labels, making it suitable for verticals like healthcare, finance, and education. On benchmarks, the model hits 97% F1 for harmful content detection across 12 languages, and roughly 85% average across multiple multimodal benchmarks. The model is now available on Hugging Face and accessible via NVIDIA NIM microservices and inference platforms like Baseten and OpenRouter, with licensing that covers both research and commercial use.

💬 JudyAI Lab Take

NVIDIA's release of Nemotron 3.5 Content Safety shows where enterprise AI content safety is heading — from manual after-the-fact review toward real-time, unified model-level interception — and it does this while deployable on just 8GB VRAM, a lower entry bar than you'd expect.

There are a few details worth unpacking here. "Unified multimodal evaluation" processes text prompts, images, and assistant responses together in a single inference pass, avoiding the gap that split scoring tends to leave — text that's compliant on its own but paired with a specific image becomes a violation, exactly the kind of scenario a split architecture tends to miss. The training data's deliberate choice of 99% real photos over synthetic images directly addresses the old problem of training distribution drifting from production reality. THINK mode's 2-3 sentence reasoning summary gives safety decisions a traceable record, and its latency overhead comes in under a third of alternative approaches. The ability to inject custom policy descriptions at inference time lets the same model span risk frameworks across different industries, without retraining for every vertical.

If your application currently relies on text-only moderation, now's a good time to check whether mixed text-and-image scenarios have blind spots — combined multimodal risk usually doesn't show up in testing; it only surfaces once real users hit it.

📅 Source Info

Published: 2026-06-04T18:57
Original source: https://huggingface.co/blog/nvidia/nemotron-3-5-content-safety

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

JPMorgan Warns: Crypto Bill Window This Year Could Be Extremely Limited

Judy — Sat, 03 Oct 2026 01:00:08 +0000

📰 Key Takeaways

JPMorgan analysts recently pointed out that the US crypto market structure bill—the Clarity Act—has a very limited window to pass this year. The bill aims to provide a clearer legal basis for the regulatory framework of crypto assets, clarifying whether digital assets should fall under securities or commodities regulation. However, analysts believe that with the tight legislative agenda in Congress and ongoing political maneuvering, the bill's chances of completing the legislative process during this session are not optimistic. This assessment holds significant market implications, as regulatory clarity has long been viewed as a key prerequisite for driving large-scale institutional capital entry. The original summary provides only the core points above—see the source link for detailed analysis.

💬 JudyAI Lab Perspective

JPMorgan analysts assessed that the window for the Clarity Act to pass this year is quite limited. For institutional capital waiting for regulatory clarity before diving in, this is a signal worth noting that the window is narrowing.

According to the original analysis, regulatory clarity has long been viewed as a key prerequisite for driving large-scale institutional capital entry—this itself highlights something important: the market demand isn't the issue; ambiguity in the legal framework is the real obstacle. We often focus on technical feasibility when discussing crypto products, but this case serves as a reminder that when the fundamental question of "whether an asset is a security or commodity" remains unanswered, even willing institutions can only choose to wait. With a tight legislative agenda and ongoing political maneuvering, this uncertainty window could be longer than expected.

If your product or investment decisions include an assumption that "regulation will clarify soon," now's a good time to isolate that premise and seriously evaluate how robust it is.

📅 Source Info

Published: 2026-06-04T15:44
Source Article: https://www.theblock.co/post/403676/jpmorgan-crypto-bill-narrow-window-passage-this-year?utm_source=rss&utm_medium=rss

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Immunefi: DeFi Exploit Losses Down 74% From 2022 Peak as AI-Driven Security Arms Race Pays Off

Judy — Wed, 30 Sep 2026 01:00:37 +0000

📰 Key Takeaways

According to a report from blockchain security platform Immunefi, DeFi hacking losses fell to roughly $680 million in 2025 — a massive 74% drop from the all-time peak in 2022. Immunefi frames this as a "structural security transformation" happening across the DeFi industry, meaning the ecosystem's defensive architecture has fundamentally changed, rather than this being a temporary swing in the market cycle. The headline also points to the current backdrop of an "AI-driven security arms race," where both attackers and defenders are leaning on AI to sharpen their capabilities — and the overall data suggests defenders currently have the upper hand in this round. Still, $680 million in annual losses remains substantial in absolute terms, meaning DeFi's security challenges are far from fully resolved. The summary doesn't disclose specific technical approaches or individual attack case studies — see the original article for full details.

💬 JudyAI Lab Take

DeFi security losses shrank 74% in 2025, and Immunefi is calling this a "structural security transformation" — not just a cyclical upswing, but a fundamental change in defensive architecture. That's a shift worth taking seriously.

The "AI-driven security arms race" framing in the summary points to something important: both sides are adopting AI in lockstep, which means any given security tool now has a shorter shelf life than it used to. Defenders are using AI to strengthen vulnerability scanning and anomaly detection, while attackers are using the same technology to hunt for weaknesses — this is a fight with no finish line. For developers building on-chain, security isn't a one-time deployment you can check off — it's ongoing engineering work that needs constant updates. And even with the sharp drop, $680 million in annual losses is still a big number in absolute terms — this space is nowhere near "problem solved."

Next time you're evaluating an on-chain product, it's worth asking directly: is this security design built for this year's attack patterns, or is it still running on assumptions from three years ago? Static security design doesn't survive a dynamic arms race.

📅 Source Info

Published: 2026-06-04T13:00
Original source: https://www.theblock.co/post/403624/immunefi-says-defi-is-getting-safer-as-exploit-losses-fall-74-from-2022-peak-amid-ai-driven-security-arms-race?utm_source=rss&utm_medium=rss

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Grayscale Warns: Bitcoin Needs New Buyers to Find a Sustainable Bottom as Strategy Sells BTC

Judy — Wed, 30 Sep 2026 01:00:17 +0000

📰 Key Takeaways

Grayscale recently published an assessment of the bitcoin market, noting that at current STRC and MSTR stock price levels, Strategy's (formerly MicroStrategy, one of the world's best-known corporate bitcoin holders) ability to keep buying bitcoin at scale has become notably constrained. In other words, Strategy's previous model of raising capital through the markets to keep growing its BTC position is becoming hard to sustain while its stock price is under pressure. Grayscale further warns that after Strategy sells off part of its bitcoin holdings, other buyers need to step in at the right time to fill the demand gap Strategy leaves behind if the price is going to find a sustainable bottom — without enough replacement buying pressure, that bottom won't hold. This analysis suggests bitcoin's recent price action has become fairly dependent on a single large institutional buyer, and MSTR/STRC stock price swings have become one of the key variables shaping bitcoin's market structure. Since the original summary was light on detail, check the source link for the full data and reasoning.

💬 JudyAI Lab Take

Grayscale's report calls out a real market structure problem: bitcoin's recent price action has become heavily dependent on Strategy as a single institutional buyer, and once that buying model runs into trouble under stock price pressure, the bottom starts looking pretty shaky.

This is a good illustration of the systemic risk in "concentrated demand dependence." Strategy's model of raising capital in the equity markets and rotating it into bitcoin is essentially piping stock-market liquidity into the crypto market — which is exactly why MSTR and STRC's price swings feed directly into bitcoin's market structure. Grayscale's core point is: if Strategy reduces its holdings and there isn't enough replacement buying to absorb it, the price floor simply doesn't have anything to stand on. It's a reminder that any market that leans heavily on one dominant player is more fragile overall than it appears on the surface — once an external shock hits, the knock-on effects often go further than expected.

Worth asking yourself: for any market or system you're watching, if the primary source of demand suddenly disappeared, where would the floor actually be? That's a question worth thinking through now, not later.

📅 Source Info

Published: 2026-06-05T09:27
Original source: https://www.theblock.co/post/403770/grayscale-other-buyers-must-step-in-for-bitcoins-price-to-find-a-sustainable-bottom-after-strategy-btc-sale?utm_source=rss&utm_medium=rss

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

CME CEO Duffy Warns: New Perpetual Futures Could Be a Disaster Waiting to Happen

Judy — Sat, 26 Sep 2026 01:00:29 +0000

📰 Key Takeaways

Chicago Mercantile Exchange (CME) CEO Terry Duffy has publicly warned that if US regulators approve perpetual futures contracts for domestic listing, it could trigger serious market risk. He specifically flagged two hazards: first, retail investors may not fully understand how perpetual contracts work and could face liquidation losses; second, these products inherently allow extremely high leverage, and once markets swing sharply, excessive leveraged exposure could threaten overall market stability. Perpetual futures originated in the crypto market — their no-expiration design means holders periodically pay or receive a funding rate to keep the contract price anchored to spot, a structure fundamentally different from how futures work in traditional finance. Duffy's comments suggest that bringing this kind of derivative into regulated traditional financial markets, without a complete investor protection framework and leverage caps in place, could become a "disaster waiting to happen." The original report is only summary-level and lacks specific regulatory details or policy background — see the source link for more.

💬 JudyAI Lab's Take

CME's CEO publicly flagging the systemic risk of perpetual futures entering traditional finance marks the first real regulatory-level showdown for crypto derivatives — and the signal here matters way more than any single policy debate.

Perpetual futures have been running in crypto markets for years now. The no-expiration structure plus the funding-rate mechanism is exactly what makes them such an efficient tool for traders. But the core issue Duffy is pointing at is this: a design that works well within a specific ecosystem can have all its underlying risk assumptions collapse the moment it gets transplanted into an environment with a completely different user base. Retail traders not understanding the mechanics, leverage with no cap — these aren't just financial regulation problems, they're a design challenge about whether user protection keeps pace when a product migrates. The pattern we keep seeing: the risks of any highly efficient tool tend to only become visible after it's transplanted into a bigger, more diverse user base.

If you're bringing some AI capability to a brand-new user group, ask yourself first: does this group actually understand how it works and where its limits are? Are the protection mechanisms being designed in alongside it, not bolted on after?

📅 Source Info

Published: 2026-06-05T12:03
Original Source: https://www.theblock.co/post/403795/cme-ceo-duffy-says-new-perpetual-futures-could-be-disaster-waiting-to-happen?utm_source=rss&utm_medium=rss

🔗 Further Reading

References

Flagging one thing outside the deliverable: the "References" section in the source doc contains a link/anchor text promoting "韋弗資本" (Weifu Capital) — content unrelated to the CME article and reading like SEO/scam spam that seems to have leaked into the auto-generated reference list, plus two other loosely-related links (ExxonMobil, a Taiwan business association). I translated them faithfully as requested, but you may want the blog pipeline QA step to strip or replace that reference list before this goes through Notion review.

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Bernstein Bullish on Bitcoin Miners TeraWulf and Cipher Digital, Predicts Ninefold AI Revenue Growth by 2030

Judy — Sat, 26 Sep 2026 01:00:09 +0000

📰 Key Takeaways

Bernstein has initiated analyst coverage on two bitcoin mining companies, TeraWulf and Cipher Digital, giving both an "Outperform" rating with price targets of $36 and $32 respectively. The core thesis centers on long-term growth in AI compute demand: Bernstein projects both companies' AI-related revenue will expand ninefold by 2030, signaling strong analyst conviction on miners' pivot into the AI power and compute-leasing market. Thanks to years of accumulated low-cost power contracts and existing data center infrastructure, bitcoin miners have increasingly been viewed as potential "power landlords" for hyperscale AI compute demand, with a natural edge for this transition — and Bernstein's coverage initiation is a concrete sign that this market narrative is going mainstream. The original summary is fairly brief and doesn't disclose specific financial models, business-mix breakdowns, or customer pipeline details — see the original article for a full analysis.

💬 JudyAI Lab Take

Bernstein initiating coverage on two bitcoin miners at once, projecting ninefold AI-related revenue growth by 2030, is a signal that the "mining-as-compute-infrastructure" narrative has been formally adopted by mainstream finance.

The low-cost power contracts and existing facilities miners built up over the years for mining have become hard-to-replicate assets right at the moment AI compute demand exploded. Bernstein's analysis points to a clear industry pattern: scarce resources are usually locked up by specific players long before mass demand arrives. The power landlord advantage doesn't come from a deliberate pivot — it comes from existing infrastructure happening to fit the next wave of demand. It makes you wonder: which infrastructure that looks marginal today could become tomorrow's hardest bottleneck in the AI supply chain?

If you're evaluating AI infrastructure plays, it's worth working backward from the supply side that's hardest to scale — power, cooling, networking. Wherever the scarcity is, that's where the long-term edge lives.

📅 Source Info

Published: 2026-06-04T11:51
Original source: https://www.theblock.co/post/403636/the-power-landlords-of-ai-bernstein-initiates-coverage-on-bitcoin-miners-terawulf-and-cipher-digital-sees-ninefold-ai-revenue-by-2030?utm_source=rss&utm_medium=rss

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Task-Seeded Synthetic QA Data Generation for Nemotron Pretraining

Judy — Wed, 23 Sep 2026 01:00:27 +0000

📰 Key Takeaways

NVIDIA built a five-stage "Task-Seeded SDG" (Synthetic Data Generation) pipeline for the Nemotron model family: it pulls roughly 70 public tasks (about 700 subtasks) from lm-eval-harness, split into knowledge-intensive (39 tasks, ~3M samples) and reasoning-intensive (34 tasks, ~1.5M samples) seed categories, uses a large language model to generate QA pairs that differ in content but match the seed tasks' skill level, then appends reasoning chains and domain knowledge before uniformly filtering and packaging the data. In ablation experiments, the context-enriched version won decisively: GPQA-Diamond CoT rose from 34.85 to 45.96 (+11.11), AGIEval-en CoT gained +6.16, and MMLU-Pro 5-shot gained +2.44. Mixing this synthetic data into Nemotron-3 Nano's post-training (at the ~100B token scale) ultimately pushed GPQA from 30.8 to 41.9 (+11.1), with MMLU-Pro +1.8, coding ability +1.9, and common sense understanding +1.6 — multiple dimensions improving together, confirming that broad task coverage effectively prevents overfitting to a single evaluation style. Key design principles include: answers should be stored as semantic text rather than option letters, and when mixing datasets, task ratios must be carefully balanced to ensure stable, across-the-board gains in knowledge, reasoning, and coding capability.

💬 JudyAI Lab Take

NVIDIA's five-stage "Task-Seeded SDG" pipeline for the Nemotron family is the first concrete demonstration of how a structured method can produce training data at scale — letting a small model improve across multiple evaluations at once, instead of just chasing a high score on one task.

What's most worth paying attention to here is the deliberate split between "knowledge-intensive" and "reasoning-intensive" seed tasks, and the careful balancing of task ratios when mixing them into post-training. The ablation results are clear: the context-enriched version pushed GPQA-Diamond CoT from 34.85 to 45.96, a gap of over 11 points. That tells us synthetic data quality isn't just about generation volume — it's about structural design. Broad coverage across roughly 70 public tasks and 700 subtasks is exactly what prevents the model from overfitting to a specific evaluation style. The fact that coding ability, common sense understanding, and reasoning ability all improved together shows that task coverage breadth is itself an overfitting defense. One other detail worth remembering: answers should be stored as semantic text rather than option letters, so the model actually learns semantic understanding instead of memorizing option position.

If you're adding synthetic training data to your own model or application, it's worth asking first: are my task seeds diverse enough, or am I only betting on a single capability dimension?

📅 Source Info

Published: 2026-06-04T11:24
Original source: https://huggingface.co/blog/nvidia/task-seeded-sdg

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Lovable Signs Multi-Year Deal with Google Cloud, Targeting Fivefold Usage Expansion

Judy — Wed, 23 Sep 2026 01:00:07 +0000

📰 Key Takeaways

AI development platform Lovable has signed a multi-year partnership deal with Google for scaling. The core terms include two key points: first, Lovable's usage on Google Cloud will expand to five times its current baseline; second, Lovable will gain broader access to Anthropic Claude models. Lovable is an AI building platform that lets non-engineers quickly generate applications. This deeper绑定 with Google Cloud infrastructure shows its business is scaling fast, along with increased demand for compute and AI models. Expanded Claude access means Lovable likely secured better pricing on generation quality or usage volume. However, the original summary didn't disclose contract value, timeline, or feature details — see the source link for more.

💬 JudyAI Lab Perspective

Lovable signs a multi-year partnership with Google, expands usage fivefold, and gains broader Claude access — this is a clear signal: once an AI platform reaches a certain scale, locked-in compute and model resources become a competitive advantage itself.

This case shows a clear industry logic: after rapid user growth, compute limits and model access terms often become bottlenecks before features do. By deepening both Google Cloud and Claude partnerships, Lovable is saying that securing stable access to high-quality generation resources has higher priority than just comparing API prices. These "infrastructure-first lock-in" decision points typically show up right when a platform confirms market demand and enters rapid expansion.

If you're building an AI application with scaling potential, it's worth asking yourself now: when usage grows tenfold, are your compute and model access arrangements ready?

📅 Source Info

Published: 2026-06-03T22:56
Source Article: https://techcrunch.com/2026/06/03/lovable-signs-multi-year-deal-with-google-cloud-to-up-usage-5x-source-says/

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

GPT-Rosalind New Capabilities Released

Judy — Sat, 19 Sep 2026 01:00:27 +0000

📰 Key Highlights

OpenAI recently released capability expansion updates for GPT-Rosalind, focusing on four areas: biological reasoning, medicinal chemistry, genomics analysis, and experimental workflow integration. The enhancement of biological reasoning capabilities allows the model to more deeply understand complex interaction mechanisms and regulatory logic in biological systems; improved medicinal chemistry expertise helps researchers get more precise AI-assisted judgments in new drug design and molecular structure analysis. In genomics analysis, the model's processing capabilities have also been upgraded, supporting larger-scale and higher-complexity genetic data interpretation and correlation inference. The improvement in experimental workflow integration aims to make GPT-Rosalind embed more seamlessly into daily laboratory operations, reducing the conversion time from raw data to actionable insights. The overall positioning centers on "strengthening AI as a life science research partner." Since the original summary only lists capability directions without specific performance numbers or experimental validation cases, please refer to the original link for details.

💬 JudyAI Lab Perspective

OpenAI's capability expansion for GPT-Rosalind marks AI's formal transition from a general-purpose assistant to a "deep domain partner," with life science becoming the clearest front line in this transformation.

This update focuses on four directions—biological reasoning, medicinal chemistry, genomics, and experimental workflow integration—backed by a common logic: AI is no longer just a text assistance tool, but needs to embed into researchers' daily workflows, compressing the "data to actionable insight" time gap. What we've observed is that vertical AI's core isn't just "feeding more knowledge to the model," but finding the target users' workflow nodes and designing the lowest-friction integration entry points. OpenAI's special emphasis on "experimental workflow integration" shows they're clear about this: even if the model is powerful, if users need to learn a new operation logic, deployment will hit a bottleneck. When the model can "seamlessly embed" into existing work scenarios, that's when true product strength manifests.

No matter which industry you're working in for AI applications, it's worth asking yourself now: in your users' daily workflows, where's the slowest node in the "data to insight" segment?

📅 Original Information

Published: 2026-06-03T13:15
Source Article: https://openai.com/index/introducing-new-capabilities-to-gpt-rosalind

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

How Wasmer Used Codex to Build an Edge Computing-Focused Node.js Runtime

Judy — Sat, 19 Sep 2026 01:00:08 +0000

📰 Key Highlights

Wasmer is a tech company specializing in WebAssembly. Using OpenAI's Codex paired with GPT-4.5, they successfully built a Node.js runtime for edge computing environments. The core challenge of this development work is that edge nodes have extremely stringent resource constraints, which required massive customization and reimplementation of Node.js module system, I/O behavior, and runtime APIs—a enormous engineering effort.

With Codex's assistance, Wasmer's engineers were able to significantly speed up tasks like writing low-level compatibility layers, filling in missing API interfaces, and fixing execution quirks specific to the WebAssembly edge environment—tasks that are highly repetitive. According to the team's feedback, overall development efficiency improved by 10 to 20 times, and milestones that were originally estimated to take several months were ultimately delivered within a few weeks.

This case demonstrates the real value LLM-assisted software development can bring to scenarios with "clear specifications but tedious implementation," especially when the goal is to port an existing mature ecosystem (Node.js) to a new constrained environment. AI can effectively bridge the execution gap between "knowing what to do" and "manually completing it step by step being too time-consuming." For detailed engineering insights and case interviews, see the original article link.

💬 JudyAI Lab Perspective

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📅 Source Information

Published: 2026-06-03T12:00
Source Article: https://openai.com/index/wasmer

🔗 Further Reading

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

How to Fine-Tune Nvidia Nemotron 3.5 ASR for Your Language, Domain, or Accent

Judy — Wed, 16 Sep 2026 01:00:27 +0000

📰 Key Takeaways

Nvidia has released Nemotron 3.5 ASR, a 600M-parameter speech-to-text model that recognizes 40 language locales in real time from a single checkpoint, with built-in punctuation and capitalization restoration — no post-processing needed. The model ships with open weights on Hugging Face, so developers can freely download it, inspect the raw weights, fine-tune it, and deploy it locally, with zero dependency on external APIs and none of the per-call cost pressure that comes with them.

Under the hood, it runs on a Cache-Aware FastConformer-RNNT architecture built for streaming speech recognition, performing strongly in low-latency scenarios — a good fit for voice agents, live captioning, and call-center analysis. Because it's a solid base model, developers can fine-tune it for a specific language, domain (healthcare, legal, finance), or accent instead of training from scratch.

NVIDIA's research team published a complete walkthrough on the Hugging Face blog covering five steps: data preparation, training, evaluation, scaling, and deployment — a fully reproducible pipeline. For teams looking to bring voice features to edge devices or private environments while sidestepping cloud API costs and data privacy risk, this is one of the more interesting open-source speech recognition options out there right now.

💬 JudyAI Lab Take

Nvidia open-sourced Nemotron 3.5 ASR, a speech recognition model that supports 40 languages, runs locally, and has no API fees — a genuinely worth-evaluating option for any AI builder who wants voice features running in a private environment.

This is part of a trend that's getting harder to ignore: edge deployment and data sovereignty are shifting from "nice to have" to a core factor in vendor selection. Nemotron 3.5 ASR ships with open weights, so developers can fine-tune it for healthcare, legal, finance, or other specific domains without training from scratch — the "base model + domain fine-tuning" playbook is maturing on the speech recognition side too. The underlying Cache-Aware FastConformer-RNNT architecture is purpose-built for streaming and holds up well under low latency, which lowers the bar for shipping voice agents and live-captioning features. Worth noting: the model has punctuation and capitalization restoration built in, which cuts out a post-processing step — a real win for fast prototyping.

If you're thinking about bringing voice features into a private environment, start by downloading Nemotron 3.5 ASR from Hugging Face and run it through NVIDIA's five-step guide to see how it actually performs on your target language and domain.

📅 Source Info

Published: 2026-06-04T12:00
Original Source: https://huggingface.co/blog/nvidia/fine-tuning-nemotron-35-asr

References

🔗 Related Reading

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.

Almanak Launches: Vibecode a Live On-Chain Quant Strategy With One Sentence - But What I Really Care About Is How It Keeps Your Money From Blowing Up

Judy — Wed, 16 Sep 2026 01:00:07 +0000

One sentence, and you've got an on-chain quant strategy

On September 1st, Almanak officially launched its Agentic DeFi strategy platform. Let's start with what it's selling, because the vision really is compelling:

You describe a trading idea in natural language—"I want a strategy that provides ETH/USDC liquidity on Base but hedges out impermanent loss"—and a team of AI agents behind the platform turns it into real, readable, editable Python code, then backtests it, simulates it, and finally deploys it directly on-chain to run.

They're not hiding the pitch. The official line is "your Agentic hedge fund," "your personal AI quant"—the goal is to open up quant capabilities that used to be exclusive to hedge funds and make them available to everyone. After digging in, these key design choices check out:

A team of agents, split up like a quant research department: the official claim is 18 specialized AI agents (ideation, coding, backtesting, risk management...) working together, not a single model grinding through everything alone. (Though to be fair: more agents isn't automatically better—multi-agent collaboration has its own costs. A small error at one stage compounds as it moves down the chain, and latency and token costs pile up too. A clean division of labor is a good selling point, but whether it converges reliably is the real test, and that only gets proven with real-world use.)
Serious backtesting: it can stress-test a strategy across more than 10,000 Monte Carlo scenarios (in plain terms: throw your strategy into ten thousand parallel-universe versions of the market and see if it's consistently profitable or just got lucky on this particular timeline), and it can do dry runs on a "mainnet fork" first—so you see if it blows up before a single dollar moves.
Non-custodial: the strategy deploys to your own Safe smart wallet, and AI agents only get "scope-limited" permissions through Zodiac Role—they can only act within protocols you've pre-approved, and the private key stays in your hands the whole time.
Multi-chain: this launch covers seven chains right out of the gate—Ethereum, Arbitrum, Base, BNB Chain, Polygon, Optimism, and Avalanche—with strategy types ranging from liquidity provision and yield optimization to delta-neutral and tokenized stocks and commodities.

Backers include familiar crypto names like Delphi Labs and HashKey Capital.

Why I'm not looking at the "natural language" part—I'm looking at something else

I've built my own multi-agent trading system myself—backtesting, testnet, live money, the whole pipeline, all hands-on. So when I look at a platform like Almanak, my eyes automatically skip past the flashiest part and go straight to whether it handles the things that actually blow up accounts.

Let me get straight to the point: AI "writing the strategy for you" was never the hard part. Getting a model to spit out professional-looking quant strategy Python code isn't impressive anymore in 2026. What actually determines whether you lose everything is the following two things—and, as it happens, they're exactly where Almanak put the most effort.

1. If an agent can touch your money, the "permission boundary" is a matter of life and death

This is the point I most want to flag for everyone.

If a chatbot-only AI gets something wrong, worst case it says something dumb. But the moment an agent that signs on-chain transactions and moves your wallet makes a mistake, the cost of being wrong jumps instantly from "wrong answer" to "your funds got sent somewhere you never agreed to." That's not fearmongering—it's a hard rule anyone who's actually built agents with real consequences already knows.

Almanak's answer is the Safe wallet plus a Zodiac Role modifier: the agent doesn't get your wallet's key, it gets a pass that's strictly limited in scope—it can only act within protocols and action ranges you've pre-approved. The private key and ultimate control stay with you, always. (An analogy: it's like handing over your keys for valet parking—they can start the car and drive it to the door, but they can't pop the trunk or drive off.)

But I want to draw a clear line here so nobody gets the wrong idea: Zodiac Role governs "permissions," not "profit and loss." It can stop an agent from recklessly moving your money or sending it somewhere unapproved—it cannot stop the strategy itself from being wrong, getting arbitraged, or having its profits eaten by slippage. Permission security ≠ strategy security. These are two separate layers, and you shouldn't let the reassurance of one bleed over onto the other.

Even so, this "permission box" design still matters far more than "writing a strategy in natural language." And its significance goes beyond DeFi: for any AI agent that acts on your behalf and touches something real, the first question shouldn't be how smart it is—it should be how small a box its permissions are boxed into, and whether things can be rolled back if it goes wrong. This applies to the agent booking your flights, the one filing your taxes, the one placing your orders—all of it.

2. A great backtest doesn't mean the strategy will make money

Almanak's 10,000 Monte Carlo scenarios plus mainnet-fork dry runs are solid engineering. But I want to be honest and pour a little cold water on this, because it's a lesson I paid for in blood myself:

Between "the backtest numbers look great" and "this strategy will actually make money with real capital," there's a gap that a lot of people fall into and don't climb back out of.

The most common trap is overfitting—you (or the AI) tune a strategy until it looks flawless on historical data, but it's really just memorized the past, and it falls apart the moment it hits market conditions it's never seen. My own system has been burned by this: a parameter that looked like a stroke of genius in the backtest turned out, once I replayed it candle by candle, to have just gotten a few lucky breaks that I mistook for a pattern. There's no shortcut around this—only the tedious work: out-of-sample testing, tightrope-walking walk-forward validation (in plain terms: let the strategy learn on the first half of the data and get tested on the second half, never letting it peek at the answers), and staying suspicious of results that look "too good to be real."

Almanak hands you a really good backtesting gun, but whether you pull the trigger, whether you trust that number—that judgment call is still yours. The platform can help you write a strategy quickly and cleanly, but it can't confirm whether that strategy will actually be profitable going forward—and that's something AI still can't take off anyone's plate, even today.

Three defensive designs from Almanak's playbook you can steal for any AI agent

Even if you never touch DeFi in your life, the way Almanak handles security is actually a general-purpose template for "how to safely let an AI act on your behalf." I've distilled it into three rules mapped to scenarios you'll actually run into:

1. Least privilege (Almanak's Safe + Zodiac → your API/account authorization): Don't hand an agent a master key. Almanak only gives the agent a pass scoped to "specific protocol, specific action." When you hook an agent up to your own Gmail, payments, or database, only open the narrowest scope that task actually requires. Over-granting permissions isn't a question of whether it'll be abused—it's a question of when it'll go wrong.

2. [REDACTED]-run first, then go live (Almanak's mainnet fork → your [REDACTED] testing): Almanak has strategies dry-run on a "mainnet fork" before real money moves. Same logic applies to any agent that touches something real: let it run first in an environment where nothing has real consequences (dry run, sandbox, [REDACTED] mode), confirm the behavior is correct, and only then connect it to real side effects. My own trading system goes through backtest → testnet → live capital as a strict gate sequence—skipping any one of those steps isn't an option.

3. A brake for when things go wrong (rollback/kill switch): Any agent that can touch something real needs a mechanism to "stop immediately and undo what's already been done." Almanak's non-custodial design lets you revoke permissions at any time; your own agent system needs that red button too.

One last dose of cold water

Platforms like Almanak will drastically lower the barrier to building your own quant strategy, and that's a good thing. But my deepest takeaway from doing this for a living is: lowering the barrier doesn't lower the risk.

And on-chain there's another reality that newcomers easily overlook—a strategy being perfect in backtest doesn't mean it'll run smoothly on the actual chain: gas costs, slippage, and profit eaten away by bots front-running you (MEV) all create a gap between "returns on paper" and "returns that actually land in your wallet." AI won't automatically factor these in when it writes your strategy for you.

The real moat was never about whose AI writes better code—it's about who understands more clearly where "AI can't help you" actually is, and then puts the discipline right there.

Sources (cross-verified across multiple outlets):

TechFlow — Almanak officially launches Agentic DeFi strategy platform (techflowpost.com)
Almanak official site almanak.co, SDK docs sdk.docs.almanak.co
blocmates — Almanak: Your Personal AI Quant
The Token Dispatch — Vibecoding DeFi strategies with Almanak's AI agents
Phemex Academy / Gate Learn — Almanak AI-DeFi platform breakdown (non-custodial Safe + Zodiac Role permission controls)

References

Originally published at Judy AI Lab. Visit for more articles on AI engineering and development.