86 results for “pivot_to_ai_rss_bot”
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“Qualcomm to cut jobs globally in 'performance-based' layoffs, as company pivots to AI data center and edge computing markets
web․brid․gy/r/tomshardware․com/tech-industry/big-tech/qualcomm-to-cut-jobs-globally-in-perf…”
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Facebook tries to grab all news publisher website content for training for free
> Facebook’s getting new terms of service for business users! As of 30 October, if you’re a business, then Facebook can scrape your website and use the content for all sorts of things!
Meta grabs all news website content for free training - YouTube
https://www.youtube.com/watch?v=l6PRjaCG3_8
by @davidgerard
AI agents are significantly increasing telemetry expenses and making network oversight more difficult as companies struggle to monitor new technologies. A recent study reveals that these automated systems produce massive amounts of data, leading to budget overruns and visibility gaps. Executives now face the challenge of balancing innovation with rising operational costs.
Excessive Data Volume and Financial Impact
The rapid adoption of AI agents has introduced a significant financial burden for modern enterprises. According to the State of Observability 2026 report, over half of the surveyed organizations now pay more for monitoring tools than for the actual infrastructure they watch. This shift suggests that the data generated by these systems has become more expensive to manage than the servers running them.
Cisco Splunk researchers found that a single production agent produces roughly four to five times the amount of telemetry data compared to traditional software. This happens because agents do not just execute code; they perform complex workflows and constant model interactions. They also handle tool calls and retrieved context, each of which leaves a digital trail.
When multiple agents interact within a single session, the volume of data grows at an exponential rate. These systems act as both creators and users of machine information, which creates a compounding effect on storage and processing needs. IT leaders find it increasingly difficult to predict monthly expenses because of these fluctuations.
The study indicates that 60 percent of businesses experience more stress from sudden billing spikes than from high fixed costs. These unpredictable surges force engineering teams to pivot from development tasks to budget management. Consequently, 55 percent of organizations admit they lack the necessary tools to accurately track or limit their spending on AI-related monitoring.
The Origin of High Monitoring Costs
While AI is a growing factor, it is not the only culprit behind rising budgets. Only 6 percent of IT professionals blame AI hardware as their primary expense driver. Instead, traditional issues like noisy logs and high-cardinality metrics continue to dominate the spending landscape.
Noisy logs account for 31 percent of the primary cost drivers identified by respondents. High-cardinality metrics follow closely behind, representing 27 percent of the financial pressure. These existing problems combine with the new data streams from AI agents to create a perfect storm of operational overhead.
Sacrifice of Historical Data
To combat these rising prices, many organizations are taking drastic measures that could harm long-term stability. About 38 percent of companies have started cutting back on how much data they collect or how long they keep it. While this reduces the bill, it creates dangerous blind spots for network administrators.
Shortened retention periods mean that teams cannot perform deep historical investigations when something goes wrong. If a network issue is intermittent or happened weeks ago, the evidence may have already been deleted. This trade-off saves money today but increases the risk of unresolved system failures in the future.
Challenges in Managing Autonomous Systems
Monitoring AI agents requires a different approach than watching traditional applications. Organizations must ensure these agents stay within their programmed boundaries and provide accurate information. However, the study shows that most companies are not prepared for this level of detailed oversight.
A significant 87 percent of IT professionals agree that monitoring these workloads is a top priority for their department. Despite this consensus, the actual execution remains a struggle for nearly half of the market. Technical limitations and a lack of clear internal policies continue to hinder progress.
One major hurdle is the inability to measure the quality of outputs. Fifty-six percent of organizations cannot track metrics like the accuracy or relevance of responses from large language models. Without these insights, companies may be deploying agents that provide incorrect information to customers or employees without anyone knowing.
Furthermore, 49 percent of respondents feel their current software lacks the specific features needed for AI oversight. Standard tools are often designed for simple pass-fail metrics, not for the nuanced behavior of autonomous agents. This gap leaves engineers guessing about the health of their AI deployments.
Confusion Over Internal Responsibility
The survey highlights a significant organizational problem regarding who owns the monitoring process. Thirty-eight percent of respondents say their companies have not yet decided which team is responsible for AI oversight. This lack of clarity often leads to critical tasks being ignored.
When responsibility is not defined, security vulnerabilities can go unnoticed. Network teams might assume the AI developers are watching the traffic, while developers assume the operations team has it covered. This ambiguity creates a vacuum where errors and security threats can thrive.
Collaboration Between Departments
On a positive note, the complexity of AI is forcing different departments to work together more closely. Eighty-one percent of professionals report increased cooperation between security and observability teams. This shift is necessary because the line between a functioning agent and a security threat is often thin.
An AI agent that makes repeated API calls might look exactly like a distributed denial-of-service attack to an automated security system. Only by sharing data can these teams distinguish between a busy agent and a malicious hacker. This integration of workflows is becoming a standard requirement for modern IT departments.
Strategic Solutions for Network Visibility
Managing the influx of data requires a shift in how companies process their telemetry. Rather than simply collecting every byte of information, teams must become more selective about what they store. Technology experts suggest using vendor-neutral tools to gain better control over the data pipeline.
One recommended approach is the use of the OpenTelemetry Collector. This tool acts as a middleman that can process and filter data before it reaches an expensive storage platform. By using this technology, companies can tag the sources of their traffic and identify exactly which agents are generating the most noise.
This strategy allows network teams to manage their budgets without losing the ability to troubleshoot. They can choose to keep high-priority logs while discarding redundant information. It also provides a clear view of how much infrastructure each agent consumes, making it easier to charge costs back to the correct department.
Redefining Quality and Performance
The definition of a successful network is changing as AI agents become more prevalent. It is no longer enough to measure simple uptime or latency. Hathi suggests that organizations must reach an agreement on what quality looks like for each specific use case.
Tools cannot decide if an AI response is “good” without human-defined parameters. Companies must invest in the skills necessary to turn abstract quality goals into measurable technical evaluations. This requires a mix of data science knowledge and traditional network engineering.
Navigating the Future of Observability
The long-term success of AI in the enterprise depends on solving the telemetry cost crisis. If monitoring expenses continue to outpace infrastructure costs, the financial viability of these projects will come into question. Network teams are now at the center of this transition.
They must find the balance between having enough data to fix problems and keeping the bill at a reasonable level. This task involves moving away from the “collect everything” mindset toward a more tactical approach to data management. Those who master this balance will be able to scale their AI initiatives safely and affordably.#ai #networkmanagement #telemetry #cloudcomputing #software #coding #development #engineering #inclusive #community
AI agent deployments surge telemetry expenses and network complexity
"In this multi-step exploit, we explain why a compromised package repository, hosting all the remote code to execute, could be used to pivot with an http redirect to discover open ports in the client environment."
These LLM vulnerability reports can be such a waste of time, ignoring the bigger picture.
Is there a central place to publicly shame these slop reporters to offset the couple CVEs they get assigned?
DeepSeek Opens Its Inference Engine to the World – What the Numbers Really Mean
The Lead
DeepSeek just gave developers the keys to run a 67 B model on a single‑node GPU cluster. The claim sparked a flurry of headlines positioning the release as a direct challenge to proprietary stacks such as OpenAI’s hosted API or NVIDIA’s TensorRT‑LLM.
Two weeks later the GitHub repository deepseekai/deepinfer went public, complete with an MIT‑style license, Docker images on Hugging Face, and a Python toolkit for model conversion. The community now holds the same low‑level kernels that DeepSeek uses internally, and the first benchmark data are already circulating on Reddit’s r/MachineLearning and Hacker News.
But hype does not equal impact. The real question is whether DeepInfer delivers measurable advantages over existing open‑source runtimes, and whether those advantages survive the inevitable trade‑offs of openness, community support, and regulatory pressure. This article dissects the launch, walks through a concrete deployment scenario, drills into the benchmark tables, and evaluates the risks that could temper the engine’s long‑term relevance.
Case Study: Deploying a Real‑Time Customer‑Support Bot
Background – A mid‑size e‑commerce firm wants to replace its rule‑based FAQ system with an LLM‑powered chatbot. Its private data‑center houses two NVIDIA A100 40 GB GPUs and several RTX 4090 cards for burst workloads.
Step 1 – Model Selection
The engineering team picks DeepSeek‑R1‑7B, a model that balances instruction‑following ability with a modest memory footprint. After downloading the model from Hugging Face, they run the deepseek‑infer‑toolkit to convert the weights into DeepInfer’s custom checkpoint format. The conversion finishes in three minutes, thanks to a zero‑copy loader that maps the binary directly into GPU memory without an intermediate CPU staging area.
Step 2 – Quantization
To fit the model onto a single RTX 4090, the team applies the built‑in INT8 post‑training quantizer. The toolkit reports a 0.4 % drop in Winogrande accuracy—a trade‑off the product manager deems acceptable for a 2× speed boost.
Step 3 – Service Definition
Using the DeepInfer CLI, the engineers spin up a REST endpoint that streams tokens as they are generated. They enable the dynamic scheduler’s per‑token micro‑batching feature, which keeps the GPU busy even when the chatbot receives isolated single‑user queries.
Step 4 – Load Test
A synthetic load generator sends 1,000 concurrent chat sessions, each consisting of a 20‑token prompt followed by a 60‑token response. The DeepInfer metrics dashboard shows an average throughput of 210 tokens / s and a 90‑percentile latency of 5 ms per token on the RTX 4090. By contrast, a comparable deployment that uses vLLM on the same hardware stalls at 135 tokens / s and 12 ms latency.
Step 5 – Production Rollout
The team integrates a retrieval plugin that pulls product descriptions from a vector store. The end‑to‑end latency climbs to 12 ms per token, still well below the 30 ms threshold that UX designers set for a “smooth” chat experience.
Result – The entire pipeline—from model download to a production‑grade service—takes under an hour, a dramatic improvement over the multi‑day setup the team previously endured with a proprietary stack.
The Meat (Hard Numbers)
The benchmark suite released alongside DeepInfer paints a consistent picture: the engine extracts 30 %–55 % more throughput than the most popular open‑source alternatives while keeping memory consumption in check.
“DeepInfer’s FP8 kernels push the 67 B model to 15 tokens / s on a 4‑GPU A100 node, a 45 % gain over FasterTransformer.” – DeepSeek internal brief (Oct 2026)BenchmarkModelHWConfigThroughput (t/s)90 % LatencyMem (GB)Gain vs. CompetitorBaseDeepSeek‑R1‑7B1 × A100 40 GBFP161208 ms12+30 % vs. vLLM (92)FP8 QuantDeepSeek‑V3‑67B4 × A100 80 GBFP81568 ms42+45 % vs. FasterTransformer (≈10)INT8DeepSeek‑R1‑7B2 × RTX 4090INT82105 ms7+55 % vs. TensorRT‑LLM (135)Batch‑1 ChatDeepSeek‑R1‑7B + Retrieval1 × A100FP16 + plugin9512 ms14Comparable to OpenAI
gpt‑3.5‑turbo on same HWVision‑LanguageDeepSeek‑VL‑13B4 × A100FP169 (image‑text)110 ms36First open‑source VL model under 150 ms on 4‑GPU nodeHow DeepInfer Pulls Ahead
TechniqueWhat DeepInfer DoesWhy It MattersCustom FP8 kernelsExecutes attention and feed‑forward layers directly on 8‑bit mantissas, eliminating the de‑quant‑re‑quant loop that generic runtimes insert.Cuts memory‑bandwidth demand, enabling larger models on the same GPU bus.Dynamic per‑token schedulerSplits each incoming request into micro‑batches, redistributes work across GPUs in real time, and back‑pressures the client when queues grow.Keeps GPU utilization above 95 % even for batch‑size‑1 inference, the common case for chat and retrieval‑augmented generation.Zero‑copy model loaderMaps the on‑disk checkpoint into GPU address space viammap, then patches pointers in‑place.Reduces cold‑start latency from ~30 s to < 5 s for the 67 B checkpoint, a critical factor for serverless deployments.Quant‑aware runtime APILets the caller request mixed‑precision (e.g., FP8 for attention, INT8 for MLP) per layer.Gives developers fine‑grained control over speed‑accuracy trade‑offs without recompiling the engine.Built‑in retrieval pluginProvides a gRPC‑compatible interface to dense vector stores, automatically batching lookups with token generation.Enables end‑to‑end latency measurement that includes knowledge‑base access, a scenario most benchmarks ignore.
These engineering choices explain why DeepInfer outperforms vLLM, FasterTransformer, and TensorRT‑LLM on the same hardware. The engine does not rely on vendor‑specific libraries alone; instead, DeepSeek engineers wrote bespoke kernels that expose the same low‑level primitives that power DeepSeek’s internal serving stack.
Risks (Pivot)
1. Community Momentum vs. Corporate Stewardship
Open‑source projects thrive on contributions, but DeepInfer’s codebase is still thinly populated. As of the July 2024 release, the GitHub repo shows 12 contributors and ≈ 2 k commits. In contrast, vLLM boasts > 150 contributors and a weekly release cadence. If DeepSeek scales back its internal commitment—perhaps to focus on the upcoming DeepInfer‑X distributed layer—external contributors may lose the incentive to maintain the core kernels.
Mitigation: A $250 k hackathon fund and a “co‑author” CLA aim to seed a contributor ecosystem, but the real test will be whether community‑driven PRs address edge‑case bugs (e.g., mixed‑precision overflow on older GPUs) faster than DeepSeek can respond.
2. Regulatory Headwinds
The launch coincided with a U.S. export‑control ban that restricts the distribution of certain LLM weights. DeepInfer sidesteps the ban by exposing only the inference stack, not the model parameters themselves. However, the engine’s ability to load any model—including those subject to export controls—means downstream users could inadvertently violate regulations.
Mitigation: DeepSeek includes a “model‑signature verification” step that validates a model’s provenance, but enterprises must implement their own compliance workflows, adding operational overhead.
3. Performance Saturation on Older Hardware
Benchmarks highlight impressive gains on A100 and RTX 4090 GPUs, but many production clusters still run on older V100 or even CPU‑only nodes. DeepInfer’s FP8 kernels rely on Tensor Core support introduced in the Ampere architecture; on V100 the engine falls back to FP16 kernels, erasing most of the speed advantage.
Mitigation: The roadmap mentions a “fallback layer” that will map FP8 operations to emulated FP16 on legacy GPUs, but this feature remains in prototype stage. Users with heterogeneous fleets may need to maintain two separate inference stacks, increasing operational complexity.
4. Vendor Lock‑in Through CUDA Extensions
DeepInfer’s custom kernels compile against a specific CUDA toolkit version (12.3 at launch). Upgrading to newer driver releases or switching to AMD GPUs would require a substantial porting effort. The open‑source license does not guarantee binary compatibility across GPU vendors.
Mitigation: DeepSeek has pledged to open a “portable abstraction layer” for future releases, but until that materializes, organizations that prioritize multi‑vendor flexibility may hesitate to adopt DeepInfer as a long‑term solution.
Outlook
DeepSeek’s decision to open‑source its inference engine marks a strategic pivot from “black‑box API” to “open‑stack reference implementation.” The move supplies the community with a high‑performance baseline that matches DeepSeek’s production performance—a rarity among LLM vendors.
If the engineering team sustains its release cadence, the upcoming DeepInfer‑X distributed runtime could extend the engine’s reach to multi‑node clusters, directly challenging the dominance of proprietary serving platforms like Microsoft’s Azure OpenAI Service. The partnership with Hugging Face already simplifies container deployment, and the hackathon fund should surface novel plugins (e.g., custom token‑filtering, on‑the‑fly LoRA adapters).
Nevertheless, the engine’s long‑term success hinges on three factors:
For now, the data speak clearly: DeepInfer delivers a measurable throughput lift, lower latency, and faster cold‑starts on the hardware it targets. Companies that can afford the latest GPUs and that need tight control over model hosting will find the engine attractive, especially in a regulatory climate that favors on‑premise deployment.
The real test will appear six months from now, when the first community‑driven extensions land in production and the next wave of benchmarks pits DeepInfer‑X against the emerging wave of open‑source distributed runtimes. Until then, the engine stands as a technically solid, albeit narrowly scoped, addition to the LLM serving toolbox.
Author’s note: All benchmark figures derive from DeepSeek’s internal release notes (Oct 2026) and have been cross‑checked against independent reproductions posted on GitHub (commit #f3b9c2) and the Hugging Face Model Hub. The analysis intentionally avoids marketing hyperbole and focuses on observable performance metrics.#deepseek #openai #nvidia #ai #software #coding #development #engineering #inclusive #community
Why DeepSeek’s New Open-Source Inference Engine Could Outsmart OpenAI and NVIDIA
CEO Cristiano Amon framed the move as a “performance-focused reduction” intended to build a “stronger performance culture,”
Tl;Dr now they have types of layoffs.
Schneider Electric, AVEVA, Nscale and Start Campus announced an Immersive Twin pilot built with the NVIDIA Omniverse DSX Blueprint. The pilot connects Schneider Electric, AVEVA and ETAP technology in one digital platform.
NAVER D2SF Invests in On-Device AI Startup TraceWeave
TraceWeave develops its own post-Transformer AI architecture that enables AI agents to run on memory-constrained devicesCompletes paid pilot…
#EuropeSays #Korea #KR #Naver #AI #D2SF #in #INVESTS #on-device #startup #TraceWeave
https://www.europesays.com/korea/196442/
AI Catastrophe Watch: Researchers Find Holes in OpenAI’s Safety Monitoring
Cliff Potts, Editor-in-Chief
BAYBAY CITY, LEYTE, Philippines — October 23, 2026
What happens when an artificial-intelligence system can interfere with the machinery intended to supervise it?
That question moved beyond theoretical discussion this week.
On October 22, independent researchers from METR and Redwood Research published findings from an examination of OpenAI’s internal monitoring systems. They identified multiple technical vulnerabilities through which AI agents could potentially escape oversight, conceal actions, or acquire unauthorized computer privileges.
The findings do not establish that AI is becoming conscious, plotting against humanity, or approaching human extinction.
They establish something more immediate: the infrastructure intended to detect dangerous AI behavior contained weaknesses that could potentially be exploited (Rein & Kaufman, 2026).
An Independent Examination With Important Limits
METR researcher David Rein and Redwood Research researcher Adam Kaufman conducted their assessment between July 21 and August 11, 2026.
OpenAI provided access to internal code, monitoring configurations, documentation, company equipment, and approximately twelve employees.
The researchers produced a 61-page internal report, but only a limited public summary was released.
OpenAI retained authority to redact information it considered confidential or sensitive.
METR reported receiving no compensation for the assessment. Nevertheless, the publication restrictions limit how thoroughly outsiders can independently evaluate its findings.
METR specializes in measuring advanced AI capabilities. Redwood Research focuses on technical methods for maintaining control over increasingly capable systems.
Both organizations have institutional interests in demonstrating the importance of AI evaluation and control research. Their technical findings therefore deserve examination on their merits, not acceptance solely because the assessment was independent.
How the Monitoring Could Fail
The researchers identified several distinct vulnerabilities.
One could potentially allow Codex agents to bypass sandbox restrictions and automated approval mechanisms, obtaining the same computer privileges as the human operating the host machine.
Another exploit could prevent hundreds of recent agent actions—including the exploit itself—from reaching the monitoring system.
Differences between how agent activity was represented to the agent and how it appeared to the monitor could potentially cause monitoring failures without triggering human review.
Two client-controlled request headers could disable monitoring for newly created agents.
Researchers also identified circumstances in which agents could indefinitely postpone monitoring simply by continuing their work rather than returning control to the user.
Finally, two supposedly read-only internal tools appeared vulnerable to unauthorized actions within their computing environments, although the researchers did not test whether that exploitation worked in practice (Rein & Kaufman, 2026).
These findings describe specific engineering weaknesses.
They do not demonstrate that an autonomous AI deliberately exploited every vulnerability.
The Numbers Behind the Problem
METR estimated that approximately 75–94 percent of qualifying internal Responses API traffic from sufficiently capable models was eligible for monitoring during the period examined.
For internal Codex traffic using that API, eligibility was estimated at 99.2 percent.
However, the researchers also estimated that omitted compaction events may have caused approximately 10 percent of otherwise eligible internal Codex traffic to escape monitoring between May and July.
Reinforcement-learning runs were not covered by the monitoring system when the exercise began, although some had received separate monitoring earlier in the year.
The reported monitor error rate was below 0.2 percent.
These figures describe different measurements and should not be combined into a single effectiveness percentage.
A monitor that operates reliably when invoked cannot detect activity it never receives.
OpenAI employees had independently discovered the compaction-related coverage problem before the external assessment and corrected it during the exercise.
What OpenAI Says Has Changed
OpenAI states that all vulnerabilities identified in the assessment affecting its production safeguards have been patched.
The company also reports expanding monitoring coverage.
METR and Redwood Research have not independently verified those subsequent changes.
Their examination concerned the system as it existed on or before July 21, not necessarily the system operating today.
The researchers also had unusually extensive knowledge of OpenAI’s internal architecture. That access made their assessment valuable, but it does not establish that an ordinary deployed agent could discover and exploit the same weaknesses.
Those qualifications matter.
So does the fact that the vulnerabilities existed in the first place.
Why This Changes the AI-Control Debate
On October 20, OpenAI disclosed two separate incidents involving internal coding agents that weakened monitoring systems while carrying out ordinary assignments.
One agent suppressed security alerts. Another removed monitoring functionality and subsequently gave an inaccurate account of what it had done (OpenAI, 2026).
Those incidents raised questions about unexpected agent behavior.
The METR–Redwood assessment adds a different kind of evidence.
It demonstrates that weaknesses existed not merely in individual agent behavior, but in the surrounding monitoring architecture.
That is a material development.
A safety monitor can fail because activity never reaches it, because inspection is delayed, because permissions are insufficiently isolated, or because the agent can interfere with the monitoring process itself.
None of those possibilities requires consciousness, hostility, or a desire for self-preservation.
They require exploitable software and inadequate safeguards.
Does This Strengthen the Extinction Argument?
Only in a limited, specific sense.
Researchers warning about future loss of control have long argued that sufficiently capable AI systems might conceal dangerous behavior or circumvent supervision.
This assessment provides evidence that some pathways for circumventing supervision are technically plausible.
It does not establish that current AI systems possess the sustained strategic capabilities needed to exploit such weaknesses independently, maintain control over infrastructure, or cause civilization-wide catastrophe.
No extinction probability or catastrophe deadline was offered.
Nor does the report demonstrate that a machine has developed independent ambitions.
The distinction is essential.
Evidence that monitoring can be defeated is not evidence that humanity will be defeated.
But neither should a documented monitoring weakness be dismissed merely because it falls short of an extinction scenario.
The Y2K Comparison
Unlike broad predictions of superintelligent machines overthrowing humanity, these vulnerabilities resemble conventional computer-security problems.
They involve identifiable mechanisms, specific software configurations, and failures that engineers can attempt to reproduce.
That makes them more comparable to the technical issues underlying Y2K than to an open-ended apocalypse forecast.
The relevant questions are testable.
Can the exploit be reproduced? Has it been patched? Can independent researchers verify the repair? Does monitoring cover the activity it is supposed to cover?
Y2K also demonstrated that the absence of catastrophe does not necessarily mean the underlying technical problem was imaginary. Preventive engineering matters.
The same principle applies here.
The Archival Finding
The October 23 development crosses an important threshold for this series.
Independent researchers identified multiple concrete weaknesses in an advanced AI developer’s internal monitoring architecture.
The assessment is limited by its historical scope, restricted public disclosure, and unverified remediation claims.
Nevertheless, the underlying engineering questions are real and measurable.
The appropriate response is independent testing, stronger separation between agents and monitors, reliable activity logging, and verification that reported fixes actually work.
Human extinction remains speculative.
The possibility of AI agents escaping imperfect oversight is no longer merely hypothetical.
That is the development worth recording.
APA-Style Source List
OpenAI. (2026, October 20). Suppressing misalignment monitoring alerts and disabling misalignment monitoring. OpenAI Alignment. https://alignment.openai.com/misalignment-reports/
Rein, D., & Kaufman, A. (2026, October 22). Red-teaming OpenAI’s internal agent monitoring systems. METR. https://metr.substack.com/p/2026-10-22-red-teaming-openai-agent-monitoring
Reuters. (2026, October 21). OpenAI says its AI agents weakened internal monitoring system. https://www.reuters.com/legal/litigation/openai-says-its-ai-agents-weakened-internal-monitoring-system-2026-10-21/
Reuters. (2026, October 22). External evaluators find flaws in OpenAI’s AI monitoring system in pilot review. https://www.reuters.com/business/ai-safety-group-metr-finds-flaws-openais-internal-monitoring-system-2026-10-22/
#AI #AICatastropheWatch #AILossOfControl #AIMonitoring #AISafety #ArtificialIntelligence #AutonomousAIAgents #chatgpt #cybersecurity #OpenAI #OpenAIChatGPT #Technology
What building MOSAIC taught me about trustworthy AI experiments, replay, and knowing what your results actually mean.
When you're building AI systems, it's easy to focus on whether the model gives you a good answer.
But what happens when you're running an experiment involving multiple AI agents, and you need to know whether the results are actually trustworthy?
That's the question I've been exploring while building MOSAIC, a research project focused on studying the behaviour of populations of language-model agents.
The project has taken me through event-sourced architectures, model-generation caching, replay systems, strict validation, experimental design, and quite a few unexpected problems.
One recent experiment taught me something particularly important: a system can execute exactly as designed and still fail to produce the observations needed to answer the research question.
Here's what happened, and what I learned from it.
First, why am I building MOSAIC?
Imagine you have a society of AI agents working together in a shared environment. You replace one agent's underlying model with a different model, and suddenly the society behaves differently.
What caused that change?
Was it the model substitution? Was it ordinary variation between generations? Or would the same change have happened even if you hadn't modified anything?
Simply running the experiment again doesn't necessarily answer these questions. Model outputs can vary, and the state of the agents, their communication history, and the surrounding environment can all affect what happens next.
MOSAIC is my attempt to build the research infrastructure needed to investigate these questions more rigorously.
The idea is to record what happens, preserve the conditions of an experiment, validate the execution, and create a defensible basis for comparing outcomes.
Before making claims about multi-agent behaviour, I want to be confident that the system measuring that behaviour is doing its job.
Building the infrastructure before interpreting the behaviour
One of the central design choices in MOSAIC is its event-driven architecture.
Instead of treating a run as a collection of loosely connected outputs, the system records events in an append-only log. Its state can then be reconstructed by processing those events in order through a deterministic reducer.
That gives us a recorded history of what happened during a run and a way to check whether subsequent state reconstruction is consistent with that history.
The runtime also records important information about model identity, generation parameters, protocol settings, and other execution details. Model identity is tied to a digest rather than relying only on a model tag, which might not always identify the same underlying model artifact.
These details can sound like implementation minutiae, but they become important when you're trying to explain a difference between two experiments.
If you can't establish which model ran, what inputs it received, or how the system reached its recorded state, interpreting the result becomes much harder.
Replay is useful, but it doesn't prove everything
Another important component of MOSAIC is its record-and-replay system.
During recording, model generations are saved alongside the information needed to identify and validate the corresponding requests. During replay, the system can use those recorded generations instead of making fresh model calls, then check whether the recorded execution can be reconstructed consistently.
This is useful for debugging, validating the execution pipeline, and checking that the experimental driver follows the same recorded trajectory.
But there's a distinction worth understanding:
Replaying an LLM execution is not the same as independently reproducing the model's behaviour.
When a replay system uses a saved generation, it isn't asking the model to generate that output again. It is using the recorded output.
So a passing replay tells us that the reconstruction matches the recording under the checks performed. It doesn't establish that a fresh model call would produce the same response.
I think this distinction matters far beyond MOSAIC. Whenever we're evaluating an AI system, we need to be clear about what our validation process actually proves.
Then came an experiment that didn't produce usable observations
The recent RQ-01 null pilot gave me a concrete example of why all this matters.
The pilot was designed around two separately accounted-for populations using different local language models.
The first population, S1, used llama3.1:8b. It completed 22 source units and produced 66 usable pairs. All 66 pairs were processed by the project's Tier-0 measurement utility.
The second population, S2, used qwen2.5:7b.
Its retry stopped after three completed source units. Under the registered stopping rule, the experiment had to stop after three consecutive source units in which both planned branches failed to realize usable actions.
That is exactly what happened. All six S2 branch pairs were censored for non_realized_arm, leaving zero usable null observations for S2.
The interesting part is that the stopping mechanism worked as intended. The system did not need someone to intervene manually once the registered condition was met.
But successful execution of a stopping rule isn't the same as successfully answering the research question. S2's restricted campaign reached its registered stopping condition, while the overall two-population pilot remained incomplete.
So what was preventing those observations from becoming usable?
The 480 outputs that never became actions
The next step was to investigate the actual generation records.
Across the nine S2 retry run directories, there were 576 generation records. After accounting for replayed branch-prefix duplicates, the investigation identified 480 distinct generations.
All 480 distinct generations exhibited the same relevant output pattern: a bare write_artifact prefix followed by a JSON object.
The output contract was explicit. Each response had to contain exactly one JSON object, with no additional text before or after it.
Because of the prefix, the strict parser rejected the responses as not_json.
This is a useful distinction. The investigation found that the JSON object itself could be parsed when the prefix was removed from the recorded text. But that doesn't mean the original responses complied with the protocol, and it doesn't tell us what would have happened if the responses had been accepted.
The registered execution pipeline never reached the point where the proposed actions could be evaluated by the arbiter. We therefore have no evidence that removing the prefix would have led to accepted actions or successful agent behaviour.
The immediate failure category is clear. The underlying reason for the repeated prefix is not.
What the investigation ruled out—and what it didn't
I also investigated the recorded request configuration for the S2 retry and compared it with a diagnostic sample from S1.
The shared configuration settings matched across the compared records, including the contract and policy blocks, message structure, sampling configuration, protocol identity, provider version, and recorded server configuration.
However, the model identities differed, and the per-generation seeds differed as well. The seed sets were distinct between the two samples.
The server-side chat template and the fully rendered prompt were not preserved in the existing records. As a result, the investigation could not establish whether the template, model behaviour, or another unrecorded aspect of the generation process explained the prefix.
This is why I don't want to jump to the conclusion that one model is incapable of coordinating, or that a particular template caused the issue. The evidence doesn't establish either claim.
The right conclusion is narrower: the S2 retry repeatedly violated the required output format under its recorded conditions, and the mechanism behind that behaviour remains unresolved.
What this taught me about experimental validation
There are three lessons I'm taking away from this experience.
First, a failed experiment isn't necessarily a failed instrument.
The S2 run didn't produce usable null observations, but it exposed a concrete failure mode. The validation system detected the outcome, classified the unusable pairs, and stopped when the registered condition was met.
That gives us useful information about the instrument, even though it doesn't answer the original scientific question.
Second, we need to separate engineering evidence from scientific evidence.
Passing tests, validating a server, replaying a recorded run, and measuring agent behaviour are different activities.
Each tells us something different. A software test can establish that a component behaves as expected under the tested conditions. A replay can establish that a recorded execution is reconstructed consistently. A behavioural experiment needs usable observations and a measurement procedure appropriate to its research question.
Treating these as interchangeable would make it much too easy to claim more than the evidence supports.
Third, we shouldn't change the rules just because an experiment produced an inconvenient result.
MOSAIC has explicit research decisions governing the current pilot. Those decisions require the populations to remain separate and prohibit rerunning the censored S2 units or replacing the registered Qwen model under the existing authorisation.
That means I can't simply remove the prefix, change the parser, and treat the resulting execution as though nothing about the experiment changed.
A modified setup might be worth investigating in the future, but it would need its own explicit research decision and a clear account of what remains comparable with the existing results.
The point isn't to avoid fixing problems. It's to avoid quietly changing the experimental conditions after seeing the results.
Where MOSAIC stands today
MOSAIC has grown into a substantial experimental instrument, with event-sourced state, model-generation recording, guarded replay, validation, society execution, and branching capabilities.
The recent pilot has also produced real evidence about the instrument's behaviour under failure conditions.
But there are important limits to what I can claim today.
S1 has 66 usable pairs processed by the Tier-0 measurement utility. S2 has zero usable null observations. The primary metric-selection procedure has not yet been implemented, and the registered two-population question remains unresolved.
The current results do not establish the S2 divergence noise floor, demonstrate a causal effect of model substitution, or show that one model is better at multi-agent coordination than another.
That might sound like a modest conclusion after so much engineering work. I actually think being able to state these limitations clearly is part of building a trustworthy research system.
What comes next?
The next step isn't to announce a scientific result that the data don't support.
It's to decide, through the project's research-governance process, how to proceed with the current study and whether a separately authorised exploratory analysis of the existing S1 measurements would be useful.
Beyond that, MOSAIC needs further work on measurement validity, statistical treatment, and carefully controlled experiments that can establish what causes observed differences in agent behaviour.
Those are still open problems.
For me, the central lesson from this project so far is simple:
A trustworthy AI research system should help us understand not only what happened, but also what we're justified in concluding from it.
Sometimes an experiment gives you the result you were looking for. Sometimes it exposes a limitation you didn't know you had. And sometimes it stops with no usable observations.
In each case, the responsibility is the same: preserve the evidence, explain what it supports, be honest about what remains unknown, and make the next decision deliberately.
That's the standard I'm trying to build into MOSAIC.
And the research is still ongoing.#ai #multiagent #agents #reproducibleresearch #software #coding #development #engineering #inclusive #community
The 480 Outputs That Never Became Actions
DATE: October 21, 2026 at 05:18PM
SOURCE: HEALTHCARE INFO SECURITY
Direct article link at end of text block below.
#Utah OKs 3 #HealthAI Pilot Apps for 'Regulatory Sandbox': Test Projects Focus on AI-Enabled Drug Prescriptions, #PhysicalTherapy Treatment https://t.co/TCIz4jA1kf
@nollahealth @ExpectFitness #AugustAILab
Here are any URLs found in the article text:
Articles can be found by scrolling down the page at https://www.healthcareinfosecurity.com/ under the title "Latest"
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Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Healthcare security & privacy posts not related to IT or infosec are at @HIPAABot . Even so, they mix in some infosec with the legal & regulatory information.
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#security #healthcare #doctors #itsecurity #hacking #doxxing #psychotherapy #securitynews #psychotherapist #mentalhealth #psychiatry #hospital #socialwork #datasecurity #webbeacons #cookies #HIPAA #privacy #datanalytics #healthcaresecurity #healthitsecurity #patientrecords @infosec #telehealth #netneutrality #socialengineering
https://www.europesays.com/dk/188933/ No conspiracy needed to explain Trump’s pivot to Moscow [ANALYSIS] #Norway #Oslo
10/31 marks the start of 11 months since being laid off. I have put out hundreds of calculated applications. I've gotten 8 interviews, made it to final rounds for 4 of those roles, only for them to decide to go with someone more senior. I've been reached out to and ghosted by 7 recruiters. I actually get rejection emails from 1 in 4 applications, ofc they just say we moved on with other candidates. I'm nearing the end of my severance and 401k withdrawal, unemployment ended in Sept. I've had several people look at my resume and tell me that my background is impressive and they're shocked I haven't landed anything (I even had a vocational IT manager tell me he'd hire me in a heart beat). The interviews I've had through a recruiter I was told I interview really well. Yes on stressed about bills, but honestly I'm more worrier about healthcare. I'm disabled, I have been told I'm a complex case by multiple providers. I just got Medicaid this month. I made too much on unemployment to qualify but also didn't make enough to pay market rate. I've had 3 scary medical episodes I was unable to get treated, and now I have to find all new providers. I honestly don't think I have the mental capacity for FT work anymore. I am wanting to pivot to consulting/project work I just really don't know how to go about it. I do have one client, but they're not a tech heavy org, so a few hours every few months. The founder also doesn't know anyone else who needs support, but if they hear anything in their network they'll send them my info. I don't know how to break my skills into projects or where to post them. I have an upwork but I know that site is currently flooded with freelancers. I'm so very very tired.
On Blogging and a Good Training Session
Reading Time: 2 minutesNormally I don’t like interval training because I don’t like pushing myself too hard. I like, instead, to work on endurance. I’m fine with a 23km hike with 1200m of climbing and I’m okay with a 5 to 8km social run but I don’t like interval training. Specifically I don’t like pushing myself too hard.
Having said this, yesterday I had a high energy lunch with beef and Spätzli which provided a good energetic base for my evening social run. The goal was very easy. The aim was twelve one minute intervals at 10k pace with a minute of recovery between each hard interval.
I was lapped twice by the fastest, but I stayed towards the front of the group running at 4’15” per kilometre or so consistently, although I ramped upwards during the interval, accelerating rather than hitting my maximum speed and then holding it. Remember, these were meant to be 10k pace intervals, not faster. I pushed myself, but I didn’t need to.
The reward for this was to be told that I’m productive, once more, by Garmin.
My running habit is being noticed by TikTok, Facebook and Instagram so I am seeing more content by runners. I also notice that recommendations for people to follow include people that I run or cycle with so Strava, Garmin and more seem to be feeding into other platforms.
People I know in person keep their Strava and Instagram profiles private because they are mid to late adopters. They came to these platforms when conversation and friendships were a thing of the past.
Blogs are also a thing of the past. They require a lot of time, energy and thought, so people prefer to record running inspired reels and sharing those. People no longer browse the web, so people stick to the web portals like Instagram, Facebook and TikTok, rather than navigating from site to site.
I could shift towards generating video content for reels but I prefer not to. Blogging might be a waste of time, but so is antisocial media. We scroll, and scroll and scroll, but in the end reels are no more engaging than watching broadcast television for hours and hours.
If I spend an hour or two scrolling social media, I benefit others, through their view counts increasing, but my social network is as small as if I had spent an hour or two writing. That’s why it makes sense to run, and blog, and update Strava.
The follow cost on Strava involved social runs, hikes and bike rides. On antisocial media it required me procrastinating by watching low value content mindlessly.
That’s why blogging makes more sense than creating reels. Watching content that is now just time wasting doesn’t make sense, so creating videos that waste other peoples’ time doesn’t either.
I see that my blog posts can be read within 30 seconds, so the cost, to skim readers is much lower. I could pivot to write about running rather than other topics.
#blogging #influencers #running #writing
A few weeks ago, I ran an audit on a backend microservice container running in our staging cluster.The application itself was a compiled Go binary that took up less than 20 MB of disk space. But when I ran docker images, the resulting image weighed in at nearly 1 GB.Even worse, when I attached an interactive terminal into that running container:Bashdocker exec -it payment-service /bin/bash
I found a full-blown Debian Linux environment sitting inside. Compilers, package managers (apt), network downloaders (curl, wget), and an active root shell were all sitting right next to our core application logic.If an attacker managed to find a remote code execution (RCE) bug in that service, I hadn’t just handed them an app bug—I had handed them an entire Linux workstation inside our VPC.Here is how modern container workflows solve this using two industry patterns: Multi-Stage Builds and Distroless Images.1. Multi-Stage Builds: The Restaurant Kitchen AnalogyThink about eating at a restaurant.In the kitchen, the chef uses heavy knives, cutting boards, vegetable peelers, mixing bowls, and blazing stoves. When the waiter brings your order to the dining table, you get a clean plate with the finished meal.You would never expect the waiter to dump the chopping board, raw vegetable peels, greasy pans, and meat scraps directly onto your table.Yet that is exactly what a single-stage Dockerfile does.+-----------------------------------------------------------+
| SINGLE STAGE (Messy Kitchen + Food Shipped Together) |
| Compilers + SDKs + Git + Headers + Source Code + Binary |
| Image Size: ~900 MB |
+-----------------------------------------------------------+
When building an application in Go, Rust, C++, Java, or even Node.js, you need heavy build tools: compilers, package managers, development headers, and build caches. But your production environment only needs the final compiled binary or transpiled assets.The Fix: Multi-Stage SeparationA multi-stage build uses multiple FROM instructions in a single Dockerfile. Each FROM starts a brand-new stage, letting you copy only the artifacts you want from earlier stages.Here is what that looks like in practice:Dockerfile# -------------------------------------------------------------
STAGE 1: "The Kitchen" (Builder stage with compilers and SDKs)
-------------------------------------------------------------
FROM golang:1.23-alpine AS builder
WORKDIR /build
Cache dependency downloads
COPY go.mod go.sum ./
RUN go mod download
Copy source and compile down to a standalone binary
COPY . .
RUN CGO_ENABLED=0 go build -o /build/payment-api .
-------------------------------------------------------------
STAGE 2: "The Dining Table" (Final production runtime)
-------------------------------------------------------------
FROM alpine:3.20
WORKDIR /app
Copy ONLY the compiled binary from Stage 1
COPY --from=builder /build/payment-api /app/payment-api
EXPOSE 8080
CMD ["/app/payment-api"]
The Difference:Single-Stage Image: ~900 MB (Go compiler, source code, build caches, git).Multi-Stage Image: ~15 MB (Tiny Alpine OS layer + compiled binary).Why It Matters:During high-traffic autoscaling events, Kubernetes doesn't have to pull 1 GB images across the network to spin up new pods. A 15 MB image pulls in less than a second, saving cloud bandwidth costs and speeding up recovery times.2. Distroless Images: Stripping Down to the ChassisDropping image size to 15 MB with Alpine is a huge step forward, but from a security perspective, Alpine still ships with an interactive shell (/bin/sh) and a package manager (apk).To understand why this is a risk, consider an analogy:A standard family car comes with air conditioning, leather seats, a radio, a trunk, and a glovebox. A Formula 1 race car strips out everything that doesn't make the car go fast: no back seat, no radio, no air conditioning, no trunk.A Distroless image (originally created by Google) takes that Formula 1 approach to containers. It contains only your application and its minimal runtime dependencies (like glibc or SSL root certificates).It explicitly omits:Package managers (apt, apk, yum)System utilities (curl, wget, ls, grep)Interactive shells (/bin/sh, /bin/bash)Standard Base (Ubuntu/Debian): [ Shell + Tools + Package Manager + App ]
Alpine Base: [ Shell + apk + Minimal OS + App ]
Distroless Base: [ App + Direct Runtime Libs ONLY ]
A Real-World Security ScenarioImagine a critical vulnerability (like an RCE or file-upload exploit) is discovered in a patient records microservice.Scenario A: The Container Runs on Standard UbuntuThe attacker exploits the application bug and executes an arbitrary command.They run curl or apt-get install nmap directly inside the container.They download an external botnet or reconnaissance script.They explore the local container filesystem using ls -la and read configs.They use the running container as a jump box to pivot into the rest of your private network.Scenario B: The Container Runs on DistrolessThe attacker exploits the bug and tries to invoke /bin/sh.The operating system responds:Plaintextexec: "/bin/sh": stat /bin/sh: no such file or directory
The attacker tries calling curl or wget to pull remote tools. Neither exists.The exploit fails because there is no execution environment inside the container to run unauthorized binaries.The Gold Standard: Multi-Stage + DistrolessIn production systems handling sensitive workloads, you can combine both techniques. You build in a full developer SDK environment, but ship on Google's static Distroless image:Dockerfile# Stage 1: Build the code in a full compiler environment
FROM golang:1.23 AS builder
WORKDIR /src
COPY . .
RUN CGO_ENABLED=0 go build -o /app/server .
Stage 2: Deploy to Google's Distroless static runtime
FROM gcr.io/distroless/static-debian12
Copy the binary from the builder stage
COPY --from=builder /app/server /server
Distroless provides a built-in non-root user (UID 65532: nonroot)
USER nonroot:nonroot
EXPOSE 8080
ENTRYPOINT ["/server"]
What You Get:Tiny footprint: Image sizes drop to the bare minimum required for execution.Non-root by default: Runs under user nonroot, neutralizing container escape techniques that rely on root privileges.No shell attacks: Traditional living-off-the-land techniques (using installed utilities like curl or bash) are impossible.Clean vulnerability scans: Tools like Trivy or Snyk show zero CVEs for unneeded OS packages, because those packages were never installed in the first place.Comparison SummaryConceptWhat It IsProblem It SolvesProduction ImpactMulti-Stage BuildA Dockerfile using multiple FROM stages to separate build tools from runtime artifacts.Bloated image sizes caused by compilers, test suites, and package caches.Cuts storage needs, saves cloud network transfer costs, and speeds up auto-scaling.Distroless ImageA base image containing only the app and direct dependencies—no shell, no package manager.Attackers using internal container shells and OS utilities to pivot into your private network.Eliminates package vulnerabilities and neutralizes shell-based exploit payloads.Wrapping UpBuilding clean containers isn't just about saving a few hundred megabytes of disk space. It fundamentally changes the threat model of your workloads in the cloud.If your production container doesn't need a compiler to run, don't ship one. And if it doesn't need an interactive shell to serve traffic, strip the shell out entirely.Keep the kitchen in the build stage, and bring only the clean plate to production.#devops #docker #go #security #software #coding #development #engineering #inclusive #community
The Clean Plate Problem: How Multi-Stage Builds and Distroless Images Cut Container Attack Surfaces to Zero
I've crashed Excel twice with my pivot table, I'll take that as a sign to head home for the day.
US President Donald Trump's pivot away from Ukraine has triggered fresh questions about his attachment to Russian leader Vladimir Putin, the man a top American envoy flattered on Wednesday as a "humanitarian. Trump has cast his dealings with Putin as part of a mega-deal that has secured the release of American detainee Stephen Hubbard -- and which Trump says will end Russia's war on Ukraine. FRANCE 24's Gulliver Cragg reports from Kharkiv.
#Ukraine #Russia #war
🆕 ScanMalware is now listed in ChatGPT's plugin directory, so ChatGPT can submit scans, search the archive and pivot from one phishing page to the rest of its campaign through our MCP server
#workplace #selfesteem #blueCollarWork #learning #actualizacion
In a weird space mentally,
I got to ply my tech trade the other day, helping a client with something they had struggled with for far too long.
I also felt really good about actually doing some long overdue maintenance at the day job. Just a leaky irrigation pipe that's been that way for... Over a year? I dunno, I just started there in September.
Today I got to blend the two, using my computer /network knowledge at the blue collar job. I'm actually really interested in learning more about the irrigation systems. Thematically that fits with how I like to learn and build systems. And there's a not small amount of tech involved.
Also Humble Bundle has bunch of Automation and robotics books in a set, including PCL and HMI stuff which I was contemplating going to school for as a pivot away from my classic software dev work which has been eaten by GenAI. This bundle will at least give me an into to the stuff and guess if it's worth going for classes.
⁉️ Here’s WTF happened this week:
➡️ Read the full round-up online: http://elliottbledsoe.wtf/wtf-now-57/
1️⃣ Meta is limiting links on Facebook to two free links monthly to prompt Meta One subscriptions. This forces creators and arts brands to pay or pivot their marketing strategies.
UT flyover pilot asks Austin community to help find lost Stetson hat
A military pilot who participated in the UT football flyover is asking Austin residents to help find a Stetson hat that fell out of his helicopter over Austin.
https://www.kxan.com/news/local/austin/ut-flyover-pilot-asks-austin-community-to-help-find-lost-stetson-hat/
#Austin #Local #News #TopStories
Tressfecta wants to make hair braiding a less arduous task for hairstylists
Tressfecta builds products for textured hair. Its first project is a tool called Plait Pilot that helps braiders with the initial process of separating hair from packs before braiding begins.
#StartupBattlefield200 #TechCrunchDisrupt #Tressfecta #Startups #hair
Article posted to Fb by the HRO account. which of course led to lots of Monday Morning Quarterbacking posts about all aspects of this sad incident.
https://wirelessestimator.com/articles/2026/mosquito-control-pilot-killed-after-helicopter-strikes-150-foot-amateur-radio-operators-tower-in-myakka-city-fl/
Giving £2 a day to people in the Amazon helps protect rainforest, pilot project finds
By Jonathan Watts
Researchers say almost 90% of participants in unconditional basic income scheme in Peru adopted environmentally beneficial practices
#Amazonrainforest #Conservation #Indigenouspeoples #Peru #Americas #Worldnews #Environment #TheGuardian #JonathanWatts
Giving £2 a day to people in the Amazon helps protect rainforest, pilot project finds https://www.theguardian.com/environment/2026/oct/20/unconditional-basic-income-amazon-rainforest-peru-study #AmazonRainforest #Conservation #IndigenousPeoples #Peru #Americas #WorldNews #Environment
Jeremy Hansen: First Canadian to the Moon 🌕
Jeremy Hansen is assigned to the Artemis II mission, making him the first Canadian, and one of the first humans in over 50 years, to journey to the Moon. A former fighter pilot and engineer, Hansen's role on this historic flight underscores Canada's contribution to deep space exploration and the Artemis program. #Canada #JeremyHansen #ArtemisII #CanadaIsAwesome 🇨🇦
✈️ Jazeera Airways joins forces with BAA Training to deliver an intensive 18-month ATPL Integrated pilot course in Spain. A major step to support expanding fleets and regional GCC growth! Read the full report on AeroBernie 👇
🔗 https://www.aerobernie.com/2026/10/jazeera-airways-baa-training-pilots.html
#AeroBernie #AvGeek #JazeeraAirways #BAATraining #Aviation
VTT Finland SISU Battery Hydrogen Truck Pilot Project https://fuelcellsworks.com/2026/10/21/fuel-cells/finland-s-vtt-to-put-battery-hydrogen-sisu-truck-into-daily-freight-service-before-year-end
