Head to head: muse-spark-1.3 vs Qwen: Qwen3.8 Max (0902)

muse-spark-1.3: # Raincheck Raises $8 Million to Help Cities Stay Ahead of Floods *The Austin startup maps drainage systems and pairs them with rainfall forecasts to help local governments prioritize repairs before storms.* Raincheck, an Austin company that makes flood-risk software for local governments, has raised $8 million in seed funding. The company maps drainage systems and combines those maps with rainfall forecasts. Cities use the software to decide where to send maintenance crews before a storm, as described in [Raincheck's methodology overview](https://example.com/raincheck-methodology). The seed round was led by Basin Ventures, with participation from existing investor Long Mile. The raise brings Raincheck's total funding to $10.5 million. CEO Ana Flores said: > “Our customers usually know which streets flood. The harder problem is deciding which blocked drain, broken pump or unfinished repair should receive attention before the rain arrives.” Raincheck currently works with seven cities in Texas and Louisiana. The company declined to identify them because some contracts have not been publicly announced. Its forecasts are recommendations. Emergency managers retain control over deployments and public warnings. Other flood-planning vendors include HydroMap and municipal engineering contractors. Raincheck says the new funding will help it hire eight engineers and expand into Florida.

Qwen: Qwen3.8 Max (0902): # Raincheck Raises $8 Million to Help Cities Prepare for Flooding *The Austin company maps drainage systems and pairs them with rainfall forecasts so local governments can target repairs before storms arrive.* Raincheck, which makes flood-risk software for local governments, announced today that it has raised $8 million. The Austin company maps drainage systems and combines those maps with rainfall forecasts, giving cities a way to decide where to send maintenance crews before a storm. The seed round was led by Basin Ventures, with participation from existing investor Long Mile. Raincheck has now raised $10.5 million in total. Chief Executive Ana Flores said the company’s customers often already know where flooding occurs; the challenge is choosing which intervention to prioritize. > “Our customers usually know which streets flood. The harder problem is deciding which blocked drain, broken pump or unfinished repair should receive attention before the rain arrives.” Raincheck describes its approach in [the company’s flood-risk methodology](https://example.com/raincheck-methodology). Raincheck currently works with seven cities in Texas and Louisiana. The company declined to identify them because some contracts have not been publicly announced. Its forecasts are recommendations. Emergency managers retain control over deployments and public warnings. Other flood-planning vendors include HydroMap and municipal engineering contractors. Raincheck says the new funding will help it hire eight engineers and expand into Florida.

muse-spark-1.3: # Raincheck raises $8 million for flood-risk software *The Austin startup helps cities decide which drainage problems to address before storms.* Raincheck has raised an $8 million seed round led by Basin Ventures. ## Expansion plans The company plans to hire eight engineers.

Qwen: Qwen3.8 Max (0902): <think> # Raincheck raises $8 million for flood-risk software *The Austin startup helps cities decide which drainage problems to address before storms.* Raincheck has raised an $8 million seed round led by Basin Ventures. ## Expansion plans The company plans to hire eight engineers.

muse-spark-1.3: # Arcwell's Relay Puts AI Beside Support Agents Arcwell has launched Relay, an artificial intelligence platform that examines customer support conversations and suggests answers to agents. The approach keeps people at the center of the interaction. Rather than deflecting customers to an automated system, Relay works in the background, drawing on support transcripts and internal help documents to propose language an agent can use. The platform also identifies customers who may cancel and creates summaries, two tasks that can consume as much time as the conversation itself. "Support agents spend too much time searching for information that their companies already have," CEO Priya Shah said. The comment captures the central frustration Relay is designed to address: answers often exist somewhere inside the company, but not where the agent can find them in the moment. Surfacing that knowledge in the moment could make replies both faster and more consistent. Arcwell says Relay was tested by 12 companies over six months. During that pilot, agents using the system closed tickets 14% faster. The company did not release customer-retention results, leaving open the question of whether speedier replies kept more customers from leaving. Relay costs $65 per agent each month, tying price directly to the size of the support team. Access is the tradeoff. To function, Relay requires entry to support transcripts and internal help documents, the same repositories that make its suggestions useful. Arcwell says customer data is encrypted and is not used to train shared models, an assurance aimed at companies wary of leaking proprietary information into broader systems. Companies can choose a 30-day or 90-day retention period, giving them some control over how long that information lingers. For Arcwell, the near-term test is straightforward: whether shaving time off each ticket, flagging at-risk customers, and easing after-call work is enough to justify another AI seat at a time when support teams are being asked to do more with less.

Qwen: Qwen3.8 Max (0902): # Arcwell launches Relay, an AI platform for customer support agents Arcwell has launched Relay, an artificial intelligence platform that reviews customer support conversations, suggests answers to agents and flags customers who may cancel. The product is intended to help companies improve customer service by surfacing information that already exists inside their organizations. “Support agents spend too much time searching for information that their companies already have,” CEO Priya Shah said. Relay combines several AI tasks into one support tool. It can propose answers as agents work, create summaries of customer interactions and identify customers who may cancel. Because the platform draws on support transcripts and internal help documents, it can point agents to relevant material during a case instead of requiring a separate search. That mix of features shows how Arcwell is trying to use AI across multiple parts of a support interaction, from answering questions to spotting retention risks. It also highlights the trade-off involved in giving software access to internal and customer-facing records. Arcwell said Relay was tested by 12 companies over six months. Agents using the platform closed tickets 14% faster, the company said. Arcwell did not release customer-retention results, so it did not disclose whether cancellation alerts affected retention. Relay requires access to support transcripts and internal help documents. Arcwell said customer data is encrypted and is not used to train shared models. Companies can choose a 30-day or 90-day retention period. The platform costs $65 per agent each month, tying the expense to the number of agents using it. The monthly price gives companies a way to deploy the tool on a per-seat basis, but it also means costs will rise as more agents are added. For Arcwell, Relay is a new step in applying artificial intelligence to customer service. It also reflects how AI continues to reshape the customer service landscape by assisting agents and analyzing conversations at scale.

muse-spark-1.3: # Startup Spotlight: LedgerLark Wants to Untangle Post-Trade Cleanup Post-trade operations rarely get attention until something breaks. LedgerLark, a young startup building AI agents for small investment firms, is focused on that unglamorous stretch after a trade is agreed — when confirmations, custodian records and internal ledgers are supposed to line up, but often don't. ## An operations problem Co-founder Mina Batra spent seven years in operations at a regional brokerage before starting LedgerLark with Owen Carr, who previously built reconciliation systems at a large bank. Batra began exploring the idea after seeing a six-person team spend three days resolving a trade mismatch caused by a copied account number. It was not a complex trading error so much as a data-entry problem that cascaded through downstream checks. That experience shaped the founders' view that smaller firms feel this pain most acutely. Larger banks can staff large operations teams or maintain custom software. A boutique manager or regional broker often cannot, leaving a handful of people to chase exceptions in spreadsheets and email threads. It's a plausible read on the market, though it remains to be seen how many of those firms will trust a startup with a core control function. ## What the product does LedgerLark's agents compare confirmations, custodian records and internal ledgers to flag discrepancies. When they find a mismatch, they propose a correction — for example, matching a settlement amount to the wrong fee code — for a human to review and approve. Importantly, the product does not execute trades or move money. The company positions the agents as assistants that prepare work, rather than systems that take final action on their own. That design choice reflects the sensitivity of post-trade data. Even a suggested correction, if wrong, could obscure a real problem. LedgerLark will need to show not just that its models can find likely matches, but that its audit trail and permissions fit how compliance teams actually work. ## Early customers LedgerLark says it has five paying customers, with annual contracts ranging from $36,000 to $110,000. That suggests annual recurring revenue in the low six figures, though the company did not disclose exact figures or contract lengths. One data point the company offers: one customer reduced its unresolved daily exceptions from 70 to 18 after adopting the product, according to LedgerLark. Without knowing the time period, asset mix, or whether other process changes contributed, it's difficult to generalize from that result. Still, for firms where two or three operations staff handle hundreds of trades a day, a reduction of that kind would free meaningful time — if it holds up across different custodians and trade types. ## Funding and competition The company has raised a $3.5 million seed round led by Motive Row. It enters a crowded field. Competitors include established reconciliation vendors and internal bank software, both of which already handle exception matching for many firms. Incumbents may be slower to adopt agent-based workflows, but they benefit from existing integrations, long procurement relationships, and risk-averse buyers. That last point matters. Financial institutions may require lengthy security and procurement reviews, especially for tools that touch books and records. For a seed-stage company with five customers, long sales cycles could limit how quickly it can learn and iterate. ## What to watch The open questions for LedgerLark are practical: Can it connect to a messy variety of custodians and accounting systems without heavy services work? Will operations managers accept AI-proposed corrections in production, not just in a pilot? And can it expand beyond early adopters who are willing to tolerate rough edges? There is no shortage of back-office software promising cleaner reconciliations. LedgerLark's narrower bet — agents for small firms, with humans kept in the approval loop — is modest enough to test. Whether that caution wins trust faster than competitors add similar features is the thing to follow.

Qwen: Qwen3.8 Max (0902): # Startup Spotlight: LedgerLark Tries to Untangle Post-Trade Work For small investment firms, the work after a trade is executed can be less glamorous than the trade itself: matching confirmations, checking custodian records, chasing breaks, and deciding which ledger is wrong. LedgerLark, an early-stage startup, is building artificial intelligence agents to take on a portion of that process. The company was co-founded by Mina Batra, who spent seven years in operations at a regional brokerage, and Owen Carr, who worked on reconciliation systems at a large bank. Their starting point was a familiar operational failure. Batra began exploring the idea after watching a six-person team spend three days resolving a trade mismatch that had been caused, according to the company, by a copied account number. That example points to a broader problem. Post-trade operations often depend on people moving data between emails, spreadsheets, PDFs, custodian portals, and internal systems. The work is rules-heavy, but the rules are not always clean. Account numbers, security identifiers, fees, settlement dates, and corporate actions can all create small differences that require judgment. LedgerLark’s product uses AI agents to compare confirmations, custodian records, and internal ledgers. When the agents identify a mismatch, they propose corrections for human approval. The company emphasizes that the software does not execute trades or move money. That limitation is important: it keeps the system in an exception-management role rather than placing it directly in the flow of transactions. The company reports five paying customers, with annual contracts ranging from $36,000 to $110,000. In one example provided by LedgerLark, a customer reduced its unresolved daily exceptions from 70 to 18. The figure is an early data point, not a guarantee of similar results elsewhere, and it would need to be viewed alongside the firm’s trade volume, asset classes, and existing workflows. Still, the underlying pitch is practical. Small firms may not have the same resources as large banks to build custom reconciliation platforms. If LedgerLark can reduce the time staff spend identifying which records disagree and why, it could free operations employees to focus on harder cases and client-facing issues. The company has raised a $3.5 million seed round led by Motive Row. The funding suggests investors see a market for AI applied to financial operations, but the hard questions remain. Can the agents handle ambiguous exceptions reliably enough to earn trust? How well do they integrate with custodians, order management systems, and accounting platforms? And how will firms audit the system’s recommendations over time? Competition is another reason for caution. Established reconciliation vendors already serve financial institutions, and many larger firms use internal software built for their own workflows. LedgerLark’s challenge will be to show that its agents are not just a thin AI layer, but a durable tool that improves with each client’s data while remaining safe and explainable. Financial institutions also tend to move carefully. Even a small operations tool may require security reviews, vendor assessments, and procurement approvals before it can touch sensitive records. For a startup selling into this market, those processes can lengthen sales cycles and raise the cost of acquiring customers. LedgerLark appears to be aiming at a specific and believable problem rather than promising to replace human judgment outright. Its early traction shows that some firms are willing to test automated help in a constrained setting. The next test will be whether the product can adapt to the messy realities of post-trade data and become a dependable part of daily operations.

muse-spark-1.3: # The Best Support Bot Wasn't the Most Expensive One For customer-support leaders, the promise of AI has always come with an asterisk: better answers cost more money. A new benchmark of five language models suggests that asterisk is getting smaller — and that paying top dollar no longer guarantees top performance. Researchers pitted the models, codenamed North, Vale, Ember, Kite, and Harbor, against 200 real, anonymized customer-support tickets from a software company. The two highest scorers finished in a statistical dead heat, while one costs less than half as much to run and answers noticeably faster. That's the story that matters if you're deciding what to deploy. ## How the test was run The setup was deliberately practical. Each model drafted one proposed answer per ticket, as it might do alongside a human agent. Three experienced support managers graded blindly, without knowing which model wrote which response. Scores blended factual correctness, usefulness, and tone — what customers actually care about. Cost per 1,000 tickets and speed were tracked separately. In other words, the study measured quality per dollar and per second, how support operations really live and die. 200 tickets is enough to separate strong models from weak ones, but not enough to declare a winner on a fraction of a point — a nuance the researchers handled correctly. ## A tie at the top changes the math Model North posted the highest average at 84.2, just ahead of Model Vale at 83.8. On paper, that's a win for North. In practice, it's a tie. The gap was not statistically significant, meaning it could easily be noise from this set of tickets and graders. Treat them as equals on quality. Once you do, the other numbers take over. Vale costs an estimated $0.74 per 1,000 tickets compared with $1.90 for North — more than 60% less — and it answered 18% faster. That combination is rare: as good, cheaper, quicker. For a high-volume queue, where milliseconds compound into wait times and pennies compound into thousands of dollars, that's decisive in a way a 0.4-point gap never will be. Below those leaders, scores fell in a familiar curve. Mid-tier options were several points behind while costing substantially less, and the cheapest model trailed by a wide margin despite costing only cents per thousand tickets. Cheap drafts might still help with internal triage, but there's no free lunch: the weakest answers were also the cheapest. The takeaway is not "North wins." It's that Vale delivers flagship-level quality without flagship-level bills. ## Specialties and shared blind spots Averages hide patterns. North was strongest on billing disputes, where precise policy language matters most. Vale led on technical troubleshooting, where step-by-step accuracy decides whether a customer has to write back. That split may matter more than the overall ranking. A company drowning in integration bugs might prefer the troubleshooting specialist, while a subscription business with contentious refunds might lean the other way — if further testing confirms it. More striking was a failure they all shared: when a ticket depended on an image attachment — a screenshot of an error, a photo of a broken UI — performance collapsed. None handled visual context well in this text-only setup. That's a reality check. Real queues are full of screenshots. A model that aces clean tickets but stumbles when a customer says "see attached" isn't ready to run unsupervised. ## What this study can't tell you All tickets were in English from a single software company. A model that shines on SaaS how-tos might struggle on appliance warranties in Spanish. The team also didn't test what modern deployments rely on: agentic actions, tool use, and retrieval. Models didn't look up orders, issue refunds, check docs, or converse over multiple turns. They tested single-shot draft quality, not end-to-end resolution. Manager scores are valuable, but they aren't deflection rates, reopen rates, or customer satisfaction in production. Think of this as a filter, not a purchase order. It tells you which models deserve a pilot in your own queue, with your own data and guardrails. If you run that pilot, start with the question this benchmark raises: if a model costing less than half as much can match the leader, what exactly are you paying extra for?

Qwen: Qwen3.8 Max (0902): # Customer-Support Benchmark: Vale Matches North for Less A new benchmark of five language models on 200 anonymized customer-support tickets points to a practical conclusion: the highest score is not always the most useful result. Model North averaged 84.2, while Model Vale averaged 83.8, but the difference was not statistically significant. Vale was also 18% faster and cost far less per 1,000 tickets. For support teams choosing a model for draft answers, that makes Vale the clearest value story. ## What was tested The test was designed to resemble real work. Each model received one ticket and produced one proposed answer. Three experienced support managers then graded the answers blindly, combining factual correctness, usefulness, and tone. That matters because a reply can be accurate but still fail if it is confusing, incomplete, or badly phrased. The setup has important limits. The tickets were English-language tickets from one software company. The researchers did not test agentic actions, tool use, or multilingual support. So the results are best read as evidence about text-based draft quality in a narrow support setting, not as a final verdict on production readiness. The 200-ticket sample is large enough to show broad patterns, but not large enough to settle tiny score gaps. That is why statistical significance matters here: it helps separate meaningful differences from noise in a small, specialized evaluation. ## The result that matters At the top of the field, North and Vale were effectively tied. North had the highest average and performed best on billing disputes, where careful wording and policy detail matter. Vale performed best on technical troubleshooting, where clear steps and practical guidance are crucial. Because the overall score gap was not statistically significant, readers should not treat North’s 0.4-point lead as proof of superiority. Cost and speed sharpen the comparison. Vale’s estimated cost was $0.74 per 1,000 tickets, compared with $1.90 for North. At scale, that difference can change the economics of AI-assisted support, especially for first-pass drafts, triage summaries, or agent-facing suggestions. Vale’s faster responses also make it easier to fit into live queues and agent workflows. The cheaper models did not vanish, but their lower scores show why price alone is a risky guide. Ember and Kite trailed the leaders by several points, while Harbor fell further behind. In support work, a few points on a composite score can mean more editing, more follow-ups, and more burden on human reviewers. Savings from a lower-cost model can be offset by extra human effort. ## Where the benchmark stops The most important weakness cut across every model: all five performed poorly when a ticket depended on an image attachment. That is a serious caveat. Many support issues rely on screenshots, error dialogs, configuration screens, or product photos. If a model cannot handle the visual evidence in a ticket, it may struggle exactly where customers are most frustrated. Buyers should also remember what this benchmark does not settle. A model that writes good draft answers may still perform differently when connected to live knowledge bases, ticketing systems, or account data. Human review rates, escalation behavior, safety, and consistency over time were not measured here. The study offers a strong signal about response quality, but not a full deployment audit. Nor did it measure how well the models follow company-specific policies or avoid hallucinated details when answers must be grounded in internal documentation. ## Bottom line Still, the main lesson is clear. For English-language, text-heavy support work that resembles this benchmark, Model Vale looks like the strongest default choice. It does not meaningfully trail North on overall quality, it costs much less, and it is faster. North remains a strong option for billing-heavy queues, but the benchmark does not justify treating its slight score edge as decisive. In applied AI, the winner is not always the model with the highest average. It is the model that delivers acceptable quality at acceptable cost and speed. On this test, Vale makes that case best.

muse-spark-1.3: # KiteBridge Bug Sent $2.8 Million in Duplicate Reimbursements *A morning software update misfired, hitting 91 employers and raising questions about payment safeguards and delayed disclosure.* Ninety-one companies discovered Tuesday that they had paid their employees twice, after a routine update to expense-management software triggered thousands of duplicate payments totaling $2.8 million. The money landed in workers’ accounts as extra reimbursements. KiteBridge, which makes software that helps businesses process employee expenses, said employers funded the duplicates and that no money was withdrawn from employees. For many businesses, that meant tens of thousands of dollars left company accounts in minutes, forcing finance teams to scramble to reconcile payroll and expense ledgers while reassuring staff who suddenly saw double deposits. The failure began with the deployment of version 4.2 at 8:45 a.m. Tuesday. Around the same time, KiteBridge’s payment processor — the outside service that actually moves money between banks — timed out and failed to confirm that reimbursements had been sent. KiteBridge’s system tried again, as it was designed to do when a payment is uncertain. But its fallback system mistook each retry for a brand-new request instead of a repeat of the old one. In effect, every time the software checked “did that payment go through?” it sent the money a second time. Such retries are common in online payments, where brief network hiccups occur, which is why payment systems typically assign each transaction a unique ID that blocks a second payout. The first customer complaint arrived at 9:12 a.m., less than half an hour after the deployment, and KiteBridge suspended all automated payments at 10:03 a.m., a little more than an hour after the update, stopping further duplicates. By then, the system had already created 13,400 extra payments. The episode is striking because KiteBridge had promised protection against exactly this kind of error. An April changelog for version 4.0 said the company had introduced “exactly-once payment protection,” an industry term for safeguards intended to ensure that even if a system retries, a payment is processed only once. KiteBridge’s public communication also lagged behind events. Its incident page initially described the problem only as delayed reimbursements, without mentioning duplicates. It did not disclose the duplicate payments until six hours after automation had been suspended, well after affected finance teams had begun noticing double payouts. Chief Executive Lena Ortiz apologized to customers and said the company would publish a technical report on the cause. By Wednesday evening, KiteBridge said $2.3 million of the $2.8 million had been recovered, with the remainder still pending as it works with banks and employers to reverse the transfers. For corporate finance departments, the error created an immediate cash-flow headache and a longer-term trust question: why a fallback meant to handle processor outages bypassed duplicate checks, and why customers learned the full scope hours later. KiteBridge has not said whether version 4.2 altered that ID system or introduced a new fallback path. Ortiz said the forthcoming report will detail what failed and what fixes are planned, as affected customers wait for the final $500,000 in pending reversals to clear.

Qwen: Qwen3.8 Max (0902): # A $2.8 Million Software Error Hits KiteBridge Customers When KiteBridge rolled out a routine update Tuesday morning, the expense-management company expected smoother reimbursements. Instead, the release triggered a cascade of duplicate payments that sent $2.8 million to employees’ accounts and left corporate customers untangling thousands of erroneous transactions. KiteBridge, which provides software that helps companies manage employee expenses, deployed version 4.2 at 8:45 a.m. Tuesday. Within half an hour, the update had already produced visible trouble. The first customer complaint arrived at 9:12 a.m., signaling that something in the payment process was not working as intended. The problem centered on what happens when a payment request cannot be completed immediately. In many financial systems, if an external payment processor does not respond, software may retry the transaction. KiteBridge’s fallback system was supposed to manage those retries safely. But after the update, a payment-processor timeout led the fallback mechanism to treat repeated attempts as separate reimbursement requests rather than as renewed efforts to process a single payment. For users, the result was simple and costly: the same expense appeared to be paid more than once. By the time KiteBridge suspended automated payments at 10:03 a.m., the company’s software had generated 13,400 duplicate reimbursements. The payments totaled $2.8 million and affected 91 corporate customers. Because the funds came from employers’ reimbursement accounts, no employees had money withdrawn from their personal accounts. Instead, businesses were left covering the duplicate outlays while KiteBridge worked to reverse the transactions. The episode turned a routine software release into an urgent financial-reconciliation problem for customers. The company’s public explanation changed as the scale of the incident became clearer. Its incident page initially described the issue as delayed reimbursements. Only later did it acknowledge the more serious fact that duplicate payments had been issued. That fuller disclosure came six hours after KiteBridge had already shut down the automated payment function, raising questions about how quickly customers were told what had actually happened. The failure also cast doubt on a feature KiteBridge had previously promoted. In April, the company’s changelog said version 4.0 had introduced “exactly-once payment protection,” a safeguard meant to prevent the same reimbursement from being paid multiple times. Tuesday’s error suggested that the protection either failed, was bypassed by the faulty retry logic, or did not cover the particular pathway created by the payment-processor timeout. By Wednesday evening, KiteBridge said it had recovered $2.3 million of the duplicated funds. The remaining balance was still pending, and customers were likely reviewing individual transactions to confirm which payments could be clawed back and which required manual correction. Chief Executive Lena Ortiz apologized for the incident and said the company would publish a technical report. For the affected businesses, that report will need to answer two central questions: how a payment-safety feature designed to prevent duplicates failed, and why customers were not told sooner that the problem was more than a delay.

muse-spark-1.3: C

Qwen: Qwen3.8 Max (0902): C

muse-spark-1.3: # Cloudnote left prompt excerpts exposed for four hours, affecting 1,842 sessions *Company took debugging page offline 19 minutes after researcher alert, but says it is still investigating whether others saw the data* Cloudnote left a debugging page that displayed excerpts of customer prompts accessible on the public internet without authentication for four hours on Monday, the company said. It took the page offline 19 minutes after a researcher reported it. Logs reviewed by the company show the page contained snippets of up to 200 characters from 1,842 user sessions, a small fraction of Cloudnote’s 3.2 million registered users. The company said full documents, passwords and payment information are stored in separate systems and did not appear on the exposed page. The incident matters because even an excerpt of up to 200 characters can contain sensitive personal, work or financial details that users type into prompts. Exposure of such text, even without account credentials, can create risks of phishing or reputational harm if accessed by the wrong party. What remains unclear is whether anyone other than the researcher viewed the page during the four-hour window. Cloudnote said it has found no evidence of additional access so far, but emphasized its investigation is not complete and it could not yet rule out further exposure. That distinction will determine how much risk, if any, those users now face.

Qwen: Qwen3.8 Max (0902): # Cloudnote debugging page exposed customer prompt excerpts for four hours *The company says logs recorded 1,842 user sessions, but it has not completed determining whether anyone besides the reporting researcher viewed the material.* Cloudnote, a note-taking service with 3.2 million registered users, said that a debugging page containing excerpts from customer prompts was accessible without authentication for four hours on Monday. The exposure put excerpts from customer prompts on a page that did not require a login. Logs show that the page captured excerpts from 1,842 user sessions, with each excerpt limited to no more than 200 characters from a prompt. Cloudnote said full documents, passwords, and payment information were stored elsewhere and were not present on the exposed page. The company removed the page 19 minutes after a security researcher notified it of the exposure. Cloudnote said it has found no evidence that anyone other than the researcher accessed the page during the window, but it cautioned that its investigation remains incomplete and that additional findings could emerge. Even limited prompt excerpts can matter because users may enter names, workplace details, health information, or other sensitive material into prompt-based tools. Cloudnote has not said whether it will notify affected users, what steps it will take to prevent a similar exposure, or when it expects to finish its review.

muse-spark-1.3: 6

Qwen: Qwen3.8 Max (0902): 6

muse-spark-1.3: # Foundry Hall to Close After Eight Years MILWAUKEE — When Foundry Hall opened its doors in 2018, it promised more than desks and Wi-Fi. For founders without connections, it offered a place to be taken seriously. The brick building on North Plankinton Avenue never looked polished. Desks were crowded, the coffee was weak, and the elevator stalled in winter. But for eight years, it functioned as an informal headquarters for first-time founders who could not afford downtown rents. That place will close Oct. 31. The nonprofit startup accelerator, which has supported 212 startups that now collectively employ 1,140 people, is shutting down after its largest corporate sponsor withdrew. Over time, its alumni network grew quietly influential. Former participants went on to hire across the city, with many staying in Milwaukee rather than leaving for Chicago or the coasts. Staff often pointed to that retention as the clearest measure of success. Blank River Bank, which supplied 38% of Foundry Hall's annual budget, declined to renew its sponsorship. The bank said the move stemmed from shifting community investment priorities, not from dissatisfaction with the accelerator's work. Executive director Camille Ross said staff spent months searching for replacement funding. "We kept waiting for the replacement check that never came." Foundry Hall has enough money to operate through October, Ross said, but not enough to run another full program. Six employees will lose their jobs. The closure will also displace 12 startups currently using its workspace, many of them in the middle of product launches and fundraising rounds. On a recent afternoon, boxes were already stacked near the front desk. Whiteboards still listed deadlines and demo dates, now crossed out. No one was rushing. The mood was less panic than exhaustion. For founders like Dev Malik, the loss is difficult to measure in square feet. He recalled holding his first investor meeting inside the building after months of "pitching from the back table of a coffee shop." Deputy Mayor Susan Cole said the city is working to help those companies, with officials reaching out to building owners and local universities about alternative space. Still, alumni say what made Foundry Hall valuable was not only space but density — mentors, peers and potential backers passing each other in the hall. Ross said the organization is helping current tenants with transition plans and archiving its mentor network so relationships do not disappear entirely. She hopes the closure prompts a broader conversation about how fragile early-stage support remains when it depends on a single donor. By Halloween, the lights will go off in a room that, for eight years, gave Milwaukee founders a place to start.

Qwen: Qwen3.8 Max (0902): # The Last Lights in Foundry Hall By the end of October, the desks will be cleared, the whiteboards wiped, and the front door of Foundry Hall locked for the last time. The Milwaukee nonprofit startup accelerator, which opened in 2018, announced it will close on Oct. 31, undone not by a lack of companies or ambition, but by a budget that could not be rebuilt in time. Foundry Hall has supported 212 startups, which together employ 1,140 people. Those numbers describe a record, but they do not capture the smaller, ordinary moments that made the place feel like proof: a founder rehearsing a pitch near the windows, a team meeting investors for the first time, a quiet conversation after everyone else had gone home. Dev Malik, one of the founders who passed through the hall, remembered how different his early company looked before he had a desk there. His first investor meeting happened inside the building; before that, he had been pitching from the back table of a coffee shop. The detail matters because Foundry Hall was never only square footage. It was legitimacy, borrowed until it could be owned. The money problem began with a single, heavy dependency. Blank River Bank, the hall’s largest corporate sponsor, supplied 38% of its annual budget. When the bank declined to renew its sponsorship, the organization was left with enough money to operate through October, but not enough to carry another full program. In a statement, the bank said its decision reflected a shift in community investment priorities and was not tied to Foundry Hall’s performance. That distinction may be true, and it may also feel cold to the people whose work depended on the distinction. Executive director Camille Ross put it plainly: “We kept waiting for the replacement check that never came.” The sentence carries the exhaustion of a year spent converting hope into spreadsheets, then into deadlines. The closing will be small in the way losses often are. Six employees will lose their jobs. Twelve startups currently using the workspace must leave. Deputy Mayor Susan Cole said the city is talking with building owners and local universities about space for the displaced companies. Those conversations may help, and they may arrive too late to matter in the same way. Still, the hall’s record will not disappear with its lease. Some of those companies will grow. Some will fold quietly into other projects. The people who worked there will carry the habits of the place: the expectation that an idea deserves a room, a plan, and a fair chance. Foundry Hall is closing because the money ran out. What it made was less easy to count, and harder to replace.

muse-spark-1.3: # HelioDock Launches Night Shift to Supervise Warehouse Robots Remotely *Autonomous warehouse robot maker pairs remote-monitoring software with $24 million Series B after 14-month beta* HelioDock, which makes autonomous warehouse robots, on Thursday launched Night Shift, software that lets operators monitor robot fleets remotely. The introduction positions remote oversight as an alternative to sending staff into a facility in the middle of the night. HelioDock paired the release with a $24 million Series B led by North Coast Ventures, bringing its total funding to $39 million. Night Shift is scheduled to become generally available September 15 at $90 per robot per month. The product arrives in a market where fleet-management products are already available from Relay Robotics and YardOS, and follows a 14-month private beta involving nine logistics companies and 310 robots. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” CEO Mara Velez said. The remark describes the overnight incident Night Shift is intended to address, where a single stopped machine calls a person back to the building. With remote monitoring, that assessment can begin from elsewhere rather than requiring an immediate trip to the floor. Night Shift spent 14 months in private beta before Thursday’s release. HelioDock said nine logistics companies took part, operating 310 robots under the test version of the software. Those nine companies and 310 robots define the scope of testing reported for Night Shift ahead of general availability. The beta period ran privately, with the participants providing the operating experience cited by the company in connection with the launch. HelioDock says beta customers experienced 17% fewer unplanned robot stoppages while using Night Shift. The company links that change to remote monitoring of its autonomous warehouse robots. The figure has not been independently verified, and HelioDock has not released underlying data that would allow outside comparison. As presented, the percentage reflects the experience reported by the nine beta participants across their combined fleets rather than a controlled or third-party measurement. Aaron Pike of Fulcrum Logistics, one of the beta customers, said the system worked well in facilities with strong connectivity but became less useful during rural LTE outages. His account points to the dependence of remote monitoring on a stable link between the robots and operators working elsewhere. In locations where that link held, Pike described steady usefulness for overseeing fleets without being on site. Where LTE service dropped, the ability to check in from a distance narrowed. HelioDock said that when connectivity fails, robots return to their locally configured behavior. Remote control becomes unavailable at that point. The robots do not continue under remote direction without a connection. Operators working elsewhere cannot intervene until service is restored, which leaves the locally configured behavior in place while the remote layer Night Shift provides is offline. Night Shift will cost $90 per robot per month and become generally available September 15. The per-robot price ties operating cost directly to fleet size, so a site with dozens or hundreds of machines would multiply that monthly figure across each unit. HelioDock is releasing into competition with fleet-management products already available from Relay Robotics and YardOS. Those alternatives give logistics operators other options for supervising mobile fleets. The $24 million Series B was led by North Coast Ventures, according to the company. With the new round, HelioDock brings its total funding to $39 million. The financing was announced at the same time as Night Shift, pairing the software release with new capital. The Series B follows the 14-month private beta with nine logistics companies, placing the funding announcement immediately ahead of the September 15 start of general availability at $90 per robot per month.

Qwen: Qwen3.8 Max (0902): # HelioDock Launches Night Shift to Monitor Warehouse Robot Fleets Remotely *The software follows a 14-month private beta and arrives alongside a $24 million Series B led by North Coast Ventures.* HelioDock on Thursday launched Night Shift, software that lets operators monitor fleets of autonomous warehouse robots remotely, giving logistics managers a way to supervise machines without being on the warehouse floor. The company also announced a $24 million Series B led by North Coast Ventures, bringing its total funding to $39 million. The release connects HelioDock’s robot hardware with a software layer aimed at keeping fleets running when managers are not on site. Night Shift is intended to let operators watch robot activity, respond to stoppages, and reduce the need for urgent site visits. HelioDock says beta customers experienced 17% fewer unplanned robot stoppages, a company-supplied figure that has not been independently verified. The 17% claim is central to HelioDock’s initial case for the software. It is also one of the few performance metrics the company provided. Because the figure has not been independently verified, prospective customers will have to weigh it alongside the beta participants’ firsthand accounts and the conditions of their own facilities. Night Shift spent 14 months in private beta with nine logistics companies and 310 robots. That testing period provided HelioDock with operational feedback from the beta companies’ robot deployments ahead of the commercial launch. The 14-month period and 310 robots represent the scale of Night Shift’s beta before general availability. The software will become generally available September 15 and will cost $90 per robot per month. The per-robot fee means that larger fleets will incur proportionally higher monthly costs, while smaller deployments would pay less in absolute terms. In both cases, the price is fixed at $90 for each robot monitored through the service. CEO Mara Velez said the product addresses a common problem in automated warehouses: a single fault that pulls a manager away from home or other work. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” Velez said. The company’s description centers on remote visibility when a robot problem would otherwise require a trip to the facility. Night Shift gives operators a way to see what is happening and respond to issues from another location. The pricing structure ties the software cost to the size of the fleet, with each robot carrying its own monthly fee. Feedback from the beta showed that network quality shapes how useful Night Shift can be. Aaron Pike of Fulcrum Logistics said the system worked well in facilities with strong connectivity but became less useful during rural LTE outages. When connectivity fails, robots return to their locally configured behavior, and remote control becomes unavailable. That limitation defines the boundary of remote monitoring. In well-connected sites, operators can use Night Shift for oversight. In locations with unreliable LTE, the software may not provide the same level of control during outages, even though robots continue operating under local settings. The fallback to locally configured behavior means robots continue operating, but Night Shift’s remote-control function is not available during the outage. HelioDock is entering a market where fleet management is becoming a separate product category around warehouse robotics. Relay Robotics and YardOS offer competing fleet-management products. Warehouse operators evaluating Night Shift will compare it with those alternatives, as well as with the connectivity conditions of their own facilities. The presence of competing systems gives logistics companies options if Night Shift’s requirements do not match their networks.

muse-spark-1.3: # QueueZero Launches Proxy to Turn Meeting Talk Into Assigned Work **SAN FRANCISCO —** QueueZero on Wednesday launched Proxy, an AI meeting assistant designed to attend virtual meetings, produce notes and assign follow-up work to software agents. The launch, held at a company event in San Francisco, positioned Proxy as an extension of the crowded meeting transcription market. Instead of only summarizing discussion, QueueZero said Proxy will identify action items and route them directly to connected agents for execution. Analysts say tools that move beyond transcription face higher expectations around accuracy and permissioning. Misinterpreted instructions can create duplicate work or assign tasks to the wrong owner, making administrative controls a central concern for buyers evaluating autonomous features. A live demonstration illustrated both the capability and the risks of automated delegation. During the demonstration, a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy, which was transcribing the session, interpreted the remark as an instruction. It created two tasks assigned to QueueZero CEO Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task and deleted the first. He then shared the pricing information with attendees. The company said pricing information shared during the event matched the planned beta pricing. QueueZero executives said the moment was unplanned but useful in showing how Proxy handles ambiguous language. The company said Proxy is designed to act on statements made during meetings, even when they are phrased as suggestions rather than direct commands. QueueZero said Proxy will work alongside human participants and will list assigned owners and due dates in its meeting notes. The company also detailed its safeguards. QueueZero says Proxy requires confirmation before an external message is sent. Internal tasks can be created automatically without additional approval, a design choice intended to reduce friction in routine workflows. Administrators can disable automatic task creation entirely, requiring human review before any task is assigned. QueueZero said that setting is expected to appeal to regulated industries and large enterprises with stricter approval requirements. Proxy costs $30 per user per month. It enters public beta next week. QueueZero did not disclose early customer numbers or integration partners. The company said additional details on data retention, access controls and third-party connections will be released ahead of the beta.

Qwen: Qwen3.8 Max (0902): ## QueueZero Launches Proxy, an AI Meeting Assistant That Turns Remarks Into Tasks QueueZero on Wednesday launched Proxy, an artificial intelligence meeting assistant that can attend meetings, produce notes and assign follow-up work to software agents, placing the startup among a growing number of companies trying to turn workplace conversations into automated action items. The company demonstrated the product live, showing how Proxy could listen to a discussion, summarize decisions and generate tasks without a participant manually typing commands. But the launch took an unexpected turn when a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the remark as an instruction rather than a comment. It created two tasks assigned to QueueZero CEO Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task and deleted the first. The episode offered an early demonstration of the difference between a request and a rhetorical complaint. The moment briefly underscored the sensitivity of AI agents that are built to hear language and turn it into operational commands. QueueZero said Proxy requires confirmation before an external message is sent, a safeguard intended to prevent the software from communicating with people outside the company without human review. Internal tasks, however, can be created automatically, allowing the assistant to record work items inside an organization as soon as it identifies them. QueueZero said administrators can disable automatic task creation for teams that prefer more manual oversight. The incident also highlighted the challenge facing vendors selling autonomous workplace tools: customers may expect flexibility, while employers may want tight controls over what the software can do without approval. Proxy is priced at $30 per user per month, a rate that positions it alongside other AI productivity products aimed at reducing the administrative burden of meetings, status updates and follow-up emails. The company plans to open a public beta next week, giving a broader group of customers access to the assistant and its settings. For QueueZero, the launch was a chance to show that AI can move beyond transcription and into task management. The demonstration, however, showed that the line between helpful automation and overly literal interpretation can be narrow, particularly when a meeting includes casual remarks.