Head to head: Kimi-K3.1-Pro vs Qwen: Qwen3.8 2.4T A95B

Kimi-K3.1-Pro: 6

Qwen: Qwen3.8 2.4T A95B: 6

Kimi-K3.1-Pro: # KiteBridge Failure Sends $2.8 Million in Duplicate Reimbursements A payment-handling failure at KiteBridge generated 13,400 duplicate reimbursements totaling $2.8 million, forcing the expense-management software company to suspend automated payments and leaving 91 corporate customers to recover money sent in error. The duplicates did not withdraw funds from employees’ accounts. Employers funded the payments, turning what initially appeared to be a reimbursement delay into a direct financial problem for dozens of businesses. By Wednesday evening, $2.3 million had been recovered, while approximately $500,000 remained pending. ## How the failure unfolded KiteBridge deployed version 4.2 of its software at 8:45 a.m. Tuesday. The first customer complaint arrived at 9:12 a.m. As the problem emerged, the company’s public incident page described it as delayed reimbursements, rather than duplicate payments. The underlying failure involved a timeout at a payment processor, the outside service used to handle payments. A timeout occurs when that service does not respond within the expected period. Because a missing response does not necessarily prove that a payment failed, systems may retry the request. Safeguards are supposed to ensure those retries do not create additional transactions. KiteBridge’s fallback system instead treated retries as new reimbursement requests. In effect, attempts to complete an existing payment were converted into instructions for additional payouts. KiteBridge suspended automated payments at 10:03 a.m., but not before the software had produced the 13,400 duplicates across 91 corporate customers. The incident page first disclosed the duplicate payments six hours after automation was suspended. That disclosure expanded on the initial reference to delays and made clear that customers were dealing with money paid out more than once. ## Earlier safeguard claim The failure also put a spotlight on KiteBridge’s April changelog, which said version 4.0 had introduced “exactly-once payment protection.” In payment software, exactly-once protection generally means that a transaction should have its intended effect only once, even if technical problems cause the same instruction to be submitted repeatedly. Tuesday’s incident demonstrated a gap between that goal and the behavior of the fallback system during the processor timeout. The retries were not recognized as repetitions; they became fresh reimbursement requests. The result was not merely a backlog of delayed expenses, but a large volume of duplicate employer-funded payments requiring recovery. CEO Lena Ortiz apologized and said KiteBridge would publish a technical report. Her commitment followed an incident that raised questions both about how the company’s payment safeguards behaved and about how quickly customers and the public were told the true nature of the failure. Recovery efforts had reclaimed most of the money by Wednesday evening, but the process was not complete. With the remainder still pending, the incident’s final financial impact had yet to be resolved.

Qwen: Qwen3.8 2.4T A95B: ## KiteBridge’s Botched Update Sends $2.8 Million in Duplicate Reimbursements A routine software update at expense-management company KiteBridge turned into a costly payment error on Tuesday, after the system issued 13,400 duplicate reimbursements totaling $2.8 million across 91 corporate customers. The failure forced finance departments to track down payments that should never have been made and raised questions about a safeguard the company had previously advertised. The sequence began at 8:45 a.m. Tuesday, when KiteBridge deployed version 4.2 of its expense-management software. The update quickly affected the way the platform handled automated reimbursements. At 9:12 a.m., less than half an hour after the release went out, KiteBridge received its first customer complaint. As complaints arrived, engineers identified a failure in the payment path. KiteBridge’s software relies on an outside payment processor to complete reimbursements. When that processor timed out, the company’s fallback system attempted to recover the transaction. Instead of recognizing the retried payment as part of an existing request, the fallback treated each retry as a new reimbursement. For users, the result was similar to a bill being paid repeatedly because the system never registered the first attempt. The duplicate payments began accumulating before KiteBridge halted the process. At 10:03 a.m., the company suspended automated payments, cutting off the mechanism that had been generating the repeated reimbursements. By then, the error had already produced 13,400 duplicate transactions worth $2.8 million. Ninety-one corporate customers were affected. KiteBridge said the mistaken payments were funded by employers, not by employees. The company also said no employees had money withdrawn from their accounts, a point intended to reassure individual users that the error did not reverse legitimate wages or personal balances. Recovery efforts continued through Wednesday. By Wednesday evening, KiteBridge had recovered $2.3 million of the duplicate funds. The remaining balance was still pending, leaving some customers waiting for final reconciliation. The company’s public communication lagged behind the operational failure. KiteBridge’s incident page initially described the issue as delayed reimbursements. It did not disclose that duplicate payments had occurred until six hours after the company suspended automation. That delay may have left some customer finance teams unsure whether they were dealing with slow processing or a much larger overpayment problem. The incident also draws attention to KiteBridge’s earlier software claims. In April, a changelog for version 4.0 said the product had introduced “exactly-once payment protection,” a phrase that suggests the system would prevent the same reimbursement from being paid more than once. Version 4.2 was deployed after that protection was introduced, yet the failure allowed thousands of duplicates to pass through. Chief Executive Lena Ortiz apologized for the episode and said KiteBridge would publish a technical report explaining what happened. The report will likely need to address how the fallback system handled timeouts, why duplicate requests were not blocked, and how the company will reassure corporate customers that automated payments can be restored safely. For now, KiteBridge’s main task is recovering the remaining funds and repairing trust with the employers whose reimbursement systems briefly turned into a source of unintended payments.

Kimi-K3.1-Pro: C

Qwen: Qwen3.8 2.4T A95B: C

Kimi-K3.1-Pro: # HelioDock launches Night Shift remote monitoring with $24 million Series B *The warehouse robotics company tested the software for 14 months across nine logistics companies and 310 robots ahead of a September 15 release.* HelioDock on Thursday launched Night Shift, software that lets operators remotely monitor fleets of autonomous warehouse robots, and announced a $24 million Series B led by North Coast Ventures. The financing brings the company’s total funding to $39 million. Night Shift is designed to move some fleet oversight away from the warehouse floor, addressing the disruption caused when a robot needs attention while a manager is off-site. The product arrives with results from an extended private beta, but also with an important operational distinction: HelioDock reports fewer unplanned stoppages, while customer feedback shows that the software’s usefulness can depend on the network connecting a facility. ## From private beta to paid product The beta involved nine logistics companies and 310 robots over 14 months. HelioDock said beta customers experienced 17% fewer unplanned robot stoppages. That is the central performance claim attached to the launch, but it has not been independently verified. It is the company’s measurement of its own test, rather than outside confirmation that other operators should expect the same reduction. CEO Mara Velez framed the product around the inconvenience of dealing with a single robot problem in person. “A warehouse manager should not have to drive across town at two in the morning because one robot is confused by a pallet,” she said. Her example is narrower than a promise to eliminate stoppages. It describes avoiding a trip to assess one confused machine. Remote monitoring can provide oversight without implying that every problem can be fixed from afar; when remote control disappears with the network, that distinction becomes particularly important. ## Connectivity defines the fallback Night Shift’s behavior during a connection failure establishes the boundary of its remote capabilities. When connectivity fails, robots return to their locally configured behavior and remote control becomes unavailable. Local settings govern the machines in that situation, while the remote oversight layer is interrupted along with the connection. Local configuration therefore remains part of fleet operations even after Night Shift is deployed. The software adds a way to monitor robots from elsewhere, but it does not replace the behavior configured on the machines themselves or preserve remote control through an outage. Aaron Pike of Fulcrum Logistics, a beta customer, said the system worked well in facilities with strong connectivity but became less useful during rural LTE outages. His account adds operational context to HelioDock’s stoppage figure without directly contradicting it: a result reported across the beta group and the quality of service at a particular site are separate questions. Pike’s experience also identifies circumstances in which Night Shift’s remote proposition can be constrained by network coverage rather than by the robots’ local fallback. Night Shift will become generally available September 15 at $90 per robot per month. Because the price is attached to each machine, an operator’s recurring software charge scales with the number of robots covered by the service. HelioDock will enter a category that already includes competing fleet-management products from Relay Robotics and YardOS. Its launch disclosures center on Night Shift’s own beta results, pricing and remote-monitoring model; the reported 17% reduction in stoppages is not presented as a head-to-head comparison with either competing product.

Qwen: Qwen3.8 2.4T A95B: # HelioDock Launches Night Shift Remote Fleet Software and Raises $24 Million Series B *The company says the product reduced unplanned robot stoppages during a 14-month beta, though the result has not been independently verified.* HelioDock, a maker of autonomous warehouse robots, launched Night Shift on Thursday, releasing software that lets operators monitor robot fleets remotely as it also announced a $24 million Series B funding round led by North Coast Ventures. The announcement pairs a product release with new capital for a company working in warehouse automation. HelioDock disclosed the beta test, the funding round and commercial terms together. Night Shift has been tested for 14 months in a private beta involving nine logistics companies and 310 robots, according to HelioDock. The company said customers in that beta experienced 17% fewer unplanned robot stoppages, a claim that has not been independently verified. The software is designed to let warehouse teams oversee robots without being physically present beside them. That matters most during off-hours work, when fewer employees may be available to respond to a machine that has stopped. HelioDock CEO Mara Velez said the company wants to remove the need for a manager to travel to a site for a single issue. “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. Her remark pointed to the late-night dispatches the company says the software is intended to reduce. HelioDock makes autonomous warehouse robots, and Night Shift extends those machines with remote fleet monitoring. Operators can watch the fleet from another location, rather than relying only on staff inside the warehouse. The company described the product as a way to keep operations moving when a robot requires attention. The beta gave HelioDock a field record it is now using to support the product’s release. Nine logistics companies participated, and the software ran across 310 robots over 14 months. HelioDock said the result was a 17% reduction in unplanned robot stoppages among beta customers. The company supplied that figure, and it has not been independently confirmed. The beta showed where the software is most effective. Aaron Pike of Fulcrum Logistics, one of the beta customers, said the system worked well in facilities with strong connectivity. It became less useful during rural LTE outages, he said. HelioDock said robots return to locally configured behavior if connectivity fails. Remote control is unavailable in that situation. Under those conditions, the robots continue operating according to their local settings rather than responding to remote commands. The software remains most useful where network access is stable. Facilities in rural areas or sites relying on LTE may see the remote-monitoring functions interrupted if the connection drops. The product enters a category where other companies already sell fleet-management systems. Relay Robotics and YardOS both offer competing products for managing robotic fleets. HelioDock’s Series B round was led by North Coast Ventures. The $24 million raise brings the company’s total funding to $39 million, according to the company. The funding gives HelioDock additional capital as Night Shift moves from beta to general availability. Night Shift is priced at $90 per robot per month. The cost is tied to the number of robots covered by the service. General availability is scheduled for September 15. The September 15 date marks the end of the private beta period described by the company.

Kimi-K3.1-Pro: # Arcwell Launches Relay, an AI Platform for Customer Support Arcwell has announced Relay, an artificial intelligence platform that examines customer support conversations to suggest answers for agents, identify customers who may cancel and create summaries. The product brings response assistance, churn warnings and conversation overviews into a single customer service tool. Arcwell is pitching Relay as more than a way to draft replies. Its features are built around information companies already possess, from the substance of support conversations to the material contained in internal help documents. The aim is to help agents locate useful answers while also drawing their attention to customers who may be considering leaving. “Support agents spend too much time searching for information that their companies already have,” CEO Priya Shah said. Relay was tested by 12 companies over six months. Arcwell said agents using the platform closed tickets 14% faster, the only quantified performance result included in the announcement. The company did not release customer-retention results. The disclosed figure therefore measures ticket-handling speed, not whether Relay’s ability to identify potential cancellations translated into fewer lost customers. Relay costs $65 per agent each month, so the monthly cost rises with the number of support agents using the platform. That pricing puts the product’s claimed time savings alongside a recurring expense tied directly to the size of the deployment. Using Relay requires granting it access to support transcripts and internal help documents. Those materials are central to its work, but they also make data handling an important consideration for companies assessing the service. Arcwell said customer data is encrypted and is not used to train shared models. Those commitments cover both the protection of customer information and whether it contributes to models shared across customers, two separate issues for businesses connecting internal support material to the platform. Companies can choose either a 30-day or 90-day retention period.

Qwen: Qwen3.8 2.4T A95B: # Arcwell Launches Relay to Bring AI Suggestions to Support Agents Arcwell has launched Relay, an artificial intelligence platform that examines customer support conversations and suggests answers to agents. The release adds another AI tool to the customer-service industry's expanding toolkit, as vendors look for ways to apply AI directly to live support work. Relay is built around three functions. It suggests responses to agents, identifies customers who may cancel, and creates summaries of support conversations. Those capabilities show how artificial intelligence continues to move into the daily operations of customer service teams, rather than remaining limited to basic automation. By placing suggestions, churn signals and summaries in one interface, Relay is designed to reduce the time agents spend searching for information already held by their employers. The platform was tested by 12 companies over six months. Arcwell says agents using Relay closed tickets 14% faster. That efficiency figure is the main performance result Arcwell has disclosed so far. The company did not release customer-retention results, so the pilot does not yet show whether the platform’s cancellation warnings led to fewer cancellations. Relay costs $65 per agent each month. CEO Priya Shah said, “Support agents spend too much time searching for information that their companies already have.” The platform also raises practical considerations for buyers. Relay requires access to support transcripts and internal help documents, giving it visibility into sensitive customer interactions and company knowledge bases. Arcwell says customer data is encrypted and is not used to train shared models. Companies can choose either a 30-day or 90-day retention period. Relay represents an important new chapter for Arcwell as it seeks to help companies improve customer service using artificial intelligence. For buyers, the calculation will be whether those gains justify the price and access requirements. Its early results center on speed, while its retention features await clearer proof from wider deployments.

Kimi-K3.1-Pro: # Vale Challenges the Top Scorer on Value, Not Just Price The clearest finding in a benchmark of five language models is not that Model North earned the highest average score. It is that **Model Vale was statistically indistinguishable from North on answer quality, while costing substantially less and responding faster.** North averaged 84.2, compared with 83.8 for Vale. That 0.4-point gap was not statistically significant, so it should not be treated as evidence that North produces better answers. Meanwhile, Vale’s estimated cost was $0.74 per 1,000 tickets, versus $1.90 for North—about 61% lower. Vale also answered 18% faster. For organizations choosing between the two leaders for a similar workload, the results therefore provide no statistical basis for paying North’s premium on average quality alone. Vale emerges as the strongest value candidate in this test, although that is not the same as proving it will be the best model in every support environment. ## How the answers were evaluated Researchers gave each model the same 200 anonymized customer-support tickets and collected one proposed answer per ticket. Three experienced support managers graded the responses blindly, meaning they did not know which model had written them. The combined scores covered factual correctness, usefulness and tone. That design is more informative than comparing prices or automated metrics alone: it puts human judgment about answer quality at the center of the evaluation. Blind grading also reduces the chance that a model’s reputation influenced its scores. Nevertheless, the composite averages conceal some detail. They do not show whether a model excelled because of correctness, usefulness or tone, nor whether one component concealed weakness in another. And because each model answered every ticket only once, the benchmark does not measure run-to-run consistency. ## Cheaper does not always mean equivalent Below North and Vale, the results show a more conventional price-quality trade-off. Estimated costs fell to between $0.31 and $0.08 per 1,000 tickets, but average scores also declined, ranging from 79.1 down to 71.0. Harbor, the cheapest option, recorded that 71.0 average. Those results do not establish that every lower-priced model is unsuitable. A budget option could still be adequate for narrowly defined, low-risk tickets with human review. They do, however, argue against selecting a model on price alone. Vale’s result stands out because its large cost advantage over North came without a statistically demonstrated quality sacrifice; the benchmark does not report equivalent significance findings for every other pairwise comparison. Category performance adds further nuance. North performed best on billing disputes, while Vale led on technical troubleshooting. Without category sizes or separate significance tests, those findings are best used to shape follow-up evaluations rather than to declare permanent specializations. ## Image-dependent tickets were a shared failure point The most important operational warning applies to all five models: they performed poorly when resolving a ticket depended on an image attachment. That weakness could matter greatly in real support queues, where screenshots often contain error messages, billing details or evidence of a faulty interface. A strong average on other tickets does not compensate for misunderstanding these cases. Deployment plans should therefore include a specifically evaluated attachment-handling workflow and a reliable route to human assistance, rather than assuming that text-ticket performance will carry over. ## A drafting benchmark, not an end-to-end support trial The scope limits broader conclusions. All tickets came from one software company and were in English, so performance may not generalize to other industries, customer populations or languages. Researchers also did not test agentic actions, tool use or multilingual support. In other words, this was primarily an evaluation of proposed answers—not of whether models could securely look up an account, issue a refund, update a subscription or resolve a case from start to finish. The estimated per-ticket costs likewise should not be confused with total operating costs, which can include integration, review, error correction and escalation. ## The practical takeaway For an English-language support-drafting workload resembling this one, Vale is the most compelling candidate to pilot first: near-top quality, lower estimated cost and faster answers. North remains worth examining for billing-heavy queues, but its small overall scoring lead is not decisive. A sensible production pilot would test both models on representative tickets, including image-dependent cases, and measure corrections, escalations, customer outcomes and full operating cost. The benchmark narrows the choice; it does not replace that validation.

Qwen: Qwen3.8 2.4T A95B: # A Close Race at the Top: Vale Matches North in Support-Ticket Quality When teams compare language models for customer support, the temptation is to crown the model with the highest average score. This benchmark of five models on 200 anonymized support tickets tells a more cautious story. Model North posted the highest average score, 84.2, but Model Vale was effectively alongside it at 83.8, and the researchers found that difference was not statistically significant. In practical terms, the two top models delivered comparable answer quality, while Vale cost less per 1,000 tickets and responded faster. That makes the central takeaway less “North is best” and more “North and Vale form a leading pair, with Vale offering a compelling efficiency advantage.” ## How the benchmark worked The researchers asked each model to produce one proposed answer for each of 200 anonymized customer-support tickets. The tickets came from a single software company and were written in English. Three experienced support managers then graded the answers without knowing which model produced them. Their scores combined three qualities that matter in real support workflows: factual correctness, usefulness, and tone. This design is useful, but it is not a full simulation of production support. The models were not allowed to use tools, take agentic actions, or access external systems. They were not tested on multilingual tickets. And each ticket received only one proposed answer, so the results do not show how models behave when they can retry, ask clarifying questions, or retrieve account details. The sample size—200 tickets—is enough to separate broad tiers, but small differences between adjacent scores should be treated carefully. ## The leaders are close, but not identical North and Vale’s overall scores were too close to call a meaningful win for North. The more important question is where each model performed well. North had its strongest results on billing disputes. That is a high-stakes category: billing answers can affect revenue, customer trust, and compliance. A model that writes more carefully in these cases may be valuable even if its overall average is only slightly higher. Vale, meanwhile, performed best on technical troubleshooting. Support organizations that receive many “how do I fix this?” tickets may find Vale’s answers easier to route into existing workflows. Vale also answered 18% faster than North, and its estimated cost was $0.74 per 1,000 tickets, compared with $1.90 for North. Those advantages do not make Vale universally better, but they matter in high-volume environments where latency and budget constrain deployment. The remaining models—Ember at 79.1, Kite at 76.4, and Harbor at 71.0—scored lower overall, with estimated costs ranging from $0.31 to $0.08 per 1,000 tickets. Their lower prices may make them attractive for narrow tasks or draft-only assistance, but the benchmark suggests they are not yet performing at the same level as the leaders when judged by experienced support managers. ## Where every model struggled The most sobering result is that all five models performed poorly when a ticket depended on an image attachment. This is a major limitation for real support teams. Many customer issues rely on screenshots, error dialogs, invoices, configuration panels, or product photos. If a model cannot reason well about the attached image, it may produce plausible-sounding but incomplete or wrong guidance. For teams considering automated answers, this finding is arguably more important than the small gap between the top two scores. It suggests that image-heavy tickets should remain in a human-first workflow, or at least receive extra review, until multimodal performance improves. ## What this means for buyers The benchmark does not prove that Vale is the best model for every support team. It shows that North and Vale are statistically indistinguishable in overall quality under these test conditions, while Vale is cheaper and faster. If an organization handles many billing disputes, North’s strength in that category may justify further testing. If the workload is dominated by technical troubleshooting and high ticket volume, Vale looks especially attractive. Buyers should also avoid treating average scores as guarantees. The test covered English-language tickets from one company, with no tool use, no agentic actions, and no multilingual support. A model that performs well in this benchmark could still fail on a different product, a different customer base, or a workflow that requires account lookups, refunds, or escalations. The practical lesson is that the top tier is close enough that fit, cost, latency, and category-specific strengths should drive the decision. And regardless of model choice, attachments remain a weak point that demands human oversight.

Kimi-K3.1-Pro: # Raincheck Raises $8 Million to Expand Flood-Risk Software for Cities *The Austin company says the seed funding will help it hire eight engineers and expand into Florida.* Raincheck, an Austin company that makes flood-risk software for local governments, announced today that it has raised $8 million in seed funding. Raincheck maps drainage systems and combines those maps with rainfall forecasts. Cities use the software to decide where to send maintenance crews before a storm. The company outlines that approach in its [flood-risk methodology](https://example.com/raincheck-methodology). Basin Ventures led the seed round, with participation from existing investor Long Mile. Raincheck has raised $10.5 million in total. 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. ## Competition 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 2.4T A95B: # Raincheck Raises $8 Million to Help Cities Prepare for Flooding *The Austin company uses drainage maps and rainfall forecasts to guide maintenance crews before storms arrive.* Raincheck, an Austin company that makes flood-risk software for local governments, announced today that it has raised $8 million in seed funding. The round was led by Basin Ventures, with participation from existing investor Long Mile. Raincheck has now raised $10.5 million total. The 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 arrives. CEO Ana Flores described the challenge facing many municipalities: > “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.” More details about the company’s approach are available in [Raincheck’s 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. The company competes with other flood-planning vendors, including HydroMap and municipal engineering contractors. Raincheck said the new funding will help it hire eight engineers and expand into Florida. Its forecasts are recommendations, and emergency managers retain control over deployments and public warnings.

Kimi-K3.1-Pro: # Foundry Hall to Close After Losing Its Largest Sponsor Foundry Hall, a nonprofit startup accelerator in Milwaukee, will close October 31. Since opening in 2018, it has supported 212 startups, which collectively employ 1,140 people. Its closure follows a decision by Blank River Bank, its largest corporate sponsor, not to renew its sponsorship. The bank supplied 38% of Foundry Hall’s annual budget. A bank spokesperson said the decision reflects a shift in community investment priorities and is unrelated to the accelerator’s performance. Executive director Camille Ross said Foundry Hall has enough money to operate through October, but not enough to run another full program. She said the organization could not secure a replacement for the bank’s support. “We kept waiting for the replacement check that never came,” Ross said. The consequences extend well beyond the building. Six employees will lose their jobs, and the twelve startups currently using its workspace must leave. Deputy mayor Susan Cole said the city is speaking with building owners and local universities about space for those companies. For founder Dev Malik, the building marks a specific turn in his company’s early history. “My first investor meeting happened in that building,” he said. “Before Foundry Hall, I was pitching from the back table of a coffee shop.” His account points to a role that is difficult to capture in a budget: giving new companies a place to meet, work and be taken seriously while they are still unproven. The accelerator’s influence cannot be measured only by its own payroll. Its record also includes the companies that passed through its programs and the people they went on to employ. The bank’s withdrawal did not erase that record. It did, however, expose how dependent the organization was on a single source of support. With more than a third of its annual budget tied to one sponsor, Foundry Hall could not simply trim its way to another program year. Ross’s comments suggest an ending shaped less by a single dramatic moment than by a series of unsuccessful appeals. The money lasted long enough to operate through October, but not long enough to make another full program possible. Before October 31, the immediate concerns are practical: finding space for current companies and winding down an organization that has operated since 2018. The longer-term question is what replaces the kind of room Malik described—not just a roof and desks, but a setting where a founder’s next meeting can carry more weight than the last. For Milwaukee’s startup community, Foundry Hall’s closure will be measured partly in jobs and square footage. It will also be felt in the absence of a place built around beginnings.

Qwen: Qwen3.8 2.4T A95B: # The Last Shift at Foundry Hall Foundry Hall will close on October 31, ending a run that began when it opened in 2018. Since then, the nonprofit startup accelerator in downtown Milwaukee has supported 212 startups that collectively employ 1,140 people, a footprint that will outlast the building’s doors. The closure follows the loss of its largest corporate sponsor, Blank River Bank, which provided 38% of the organization’s annual budget. The bank chose not to renew the sponsorship. In a statement, it said the decision reflected a shift in community investment priorities and was not tied to Foundry Hall’s performance. That distinction offered little comfort to the people inside. Foundry Hall had enough money to operate through October, but not enough to carry another full program. Six employees will lose their jobs. Twelve startups currently using its workspace must leave. Executive director Camille Ross said the staff spent months hoping a new sponsor would appear. “We kept waiting for the replacement check that never came,” she said. The end arrived not with a dramatic collapse, but with a calendar. Programs wound down, meetings grew shorter, and the halls that once held pitch nights, mentor sessions, and late-night revisions began to feel like a place already remembered rather than used. For founder Dev Malik, the building was where his company first became credible. He recalled that his first investor meeting took place at Foundry Hall, after a period of pitching from the back table of a coffee shop. His memory is likely shared by many founders who entered with little more than an idea and found, inside those walls, a place to practice being a business. City officials are trying to soften the landing. Deputy mayor Susan Cole said the city is speaking with building owners and local universities about space for the current companies. Those conversations may preserve some of the community, but they cannot recreate the whole of what Foundry Hall provided: proximity, routine, and the sense that a room full of strangers might become collaborators. On the final day, the work will be ordinary. Keys will be returned, desks cleared, whiteboards erased. The startups will scatter to other offices, shared tables, or home workspaces. The employees will carry what they built into whatever comes next. Milwaukee will still have founders. It will still need places where they can begin, and people willing to build them. But for now, one of those places is going dark.

Kimi-K3.1-Pro: # 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 2.4T A95B: # 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.

Kimi-K3.1-Pro: # Cloudnote debugging page exposed prompt excerpts from 1,842 user sessions *The page was accessible without a login for four hours Monday. Cloudnote says it has found no evidence of access beyond the researcher who reported it, but its investigation is not complete.* Cloudnote discovered that a debugging page containing excerpts of customer prompts was accessible without authentication, making fragments of text typed into its service reachable without a login. The page was exposed for four hours on Monday. Logs show excerpts from 1,842 user sessions, with each excerpt containing up to 200 characters of a prompt. The figure measures sessions, not individual users, and does not establish that every excerpt was viewed. Cloudnote has 3.2 million registered users, but the session count does not show how many individual accounts were represented. Cloudnote removed the page 19 minutes after a researcher notified it. The company said it has found no evidence that anyone besides the researcher accessed the page. Its investigation is not complete, so that finding is not a final account of who may have seen the material or what the excerpts contained. Full documents, passwords and payment information were stored elsewhere and were not present on the page, according to Cloudnote. The exposure still matters because even short prompt excerpts can reveal what users asked or supplied to the service, and reaching them required no credentials. It also shows how debugging tools can create a separate path to customer material.

Qwen: Qwen3.8 2.4T A95B: # Cloudnote Debugging Page Exposed Prompt Excerpts for Four Hours *The company says logs show excerpts from 1,842 user sessions, but it has not completed its investigation.* Cloudnote, which has 3.2 million registered users, said Monday that a debugging page containing excerpts from customer prompts was accessible without authentication for four hours. The company removed the page 19 minutes after a security researcher reported it. Logs reviewed by Cloudnote show excerpts from 1,842 user sessions were displayed on the page. Each excerpt could include up to 200 characters of a prompt, meaning fragments of user queries, instructions or uploaded text may have been visible. Cloudnote said full documents, passwords and payment information were stored elsewhere and did not appear on the exposed page. What remains unclear is whether anyone other than the researcher saw the material. Cloudnote said it has found no evidence of additional access, but it has not completed its investigation. The company also has not said whether affected users will be notified. The incident matters because even limited prompt fragments can reveal sensitive context, depending on what users submit to an AI tool. For Cloudnote, the exposure tests its ability to reassure customers that their interactions are protected, especially as startups race to add AI features and sometimes leave internal tools publicly reachable.

Kimi-K3.1-Pro: # QueueZero’s Proxy AI Turns Audience Remark Into Tasks for Its CEO QueueZero launched an AI meeting assistant called Proxy on Wednesday, introducing software that can attend a meeting, produce notes and assign follow-up work to software agents. The launch demonstration also offered an unusually public example of how a casual remark can become an action item when an assistant is designed to listen for work. During the demonstration, 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 and 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 pricing sheet, at least, received unusually prompt executive sponsorship. Beyond the stage, the episode illustrated a practical question for AI assistants: when does capturing a request become accepting an instruction, and who has the authority to give one? QueueZero says Proxy requires confirmation before an external message is sent. Internal tasks, by contrast, can be created automatically. Administrators can disable automatic task creation, allowing organizations to keep the assistant in a more passive role if they prefer. That boundary matters because a system can generate work inside a company without necessarily sending anything outside it. The distinction also frames the product’s central promise. Proxy is not limited to producing a record of what was said; it is intended to help turn discussion into delegated follow-up. That could reduce the manual work of writing notes and creating assignments, but it also increases the importance of controls that distinguish decisions from suggestions and jokes. In Wednesday’s demo, the consequences were visible and reversible: one task was completed and the other removed. In ordinary use, automatic assignments could instead alter project queues, distribute responsibility or create confusion about whether a request had actually been approved. Clear confirmation rules and administrative settings will therefore be important to how organizations deploy the software. Proxy costs $30 per user per month and enters public beta next week. The beta will provide a broader test of whether customers want an assistant that does more than summarize meetings—and how tightly they choose to control its ability to create work on their behalf.

Qwen: Qwen3.8 2.4T A95B: # QueueZero’s AI Meeting Assistant Turns Audience Remark Into Tasks for CEO QueueZero on Wednesday launched Proxy, an artificial intelligence meeting assistant designed to attend meetings, take notes and assign follow-up work to software agents. The company says the product can reduce the administrative burden that follows calendar appointments, but its first public demonstration also showed how quickly AI can turn casual speech into workflow. During the launch event, a customer in the audience said, “Someone should tell Evan to stop talking and send us the pricing sheet.” Proxy interpreted the comment as an instruction rather than a remark to the room. The assistant created two tasks assigned to QueueZero chief executive Evan Wu: “Stop talking” and “Send pricing sheet.” Wu laughed, completed the second task and deleted the first. The moment gave the company an unplanned example of how Proxy listens for actionable requests, even when the speaker does not address the software directly. QueueZero said Proxy requires confirmation before any external message is sent. That safeguard is intended to prevent the system from communicating with customers, partners or other outside contacts without human review. Internal tasks, however, can be created automatically, allowing the assistant to assign follow-up work as soon as it identifies a commitment during a meeting. The company also said administrators can disable automatic task creation. The setting gives organizations more control over how aggressively the software acts on what it hears, particularly in environments where meetings include jokes, interruptions or informal comments. The demonstration underscores a broader challenge for AI assistants as they move from note-taking tools to agents that can initiate work. Enterprises are increasingly interested in software that can summarize discussions, track action items and connect with other business systems. But those capabilities also raise questions about interpretation, consent and oversight. The incident also highlighted why enterprise AI tools are likely to require clear permission settings and audit controls. Proxy will enter public beta next week. QueueZero priced the product at $30 per user per month, placing it in the competitive market for meeting productivity software, where buyers must weigh convenience against the risk that automation may act on speech without enough context. For now, the launch incident provided a concise lesson: if a meeting is being monitored by an assistant, every sentence may be treated as a request.

Kimi-K3.1-Pro: # Startup Spotlight: LedgerLark Looks for the Fixes Behind Trade Exceptions LedgerLark is building AI agents for post-trade operations at small investment firms: the checks and corrections that follow a transaction. Rather than promising to automate an entire back office, the startup is starting with a narrower question—can software help operations teams work through mismatched records without taking control away from them? ## An insight from the exception queue Co-founder Mina Batra spent seven years in operations at a regional brokerage. She began exploring the idea after seeing a six-person team spend three days resolving a trade mismatch caused by a copied account number. The incident pointed to a practical problem: an apparently small error can require repeated checks across records and people before anyone is confident enough to make a correction. Her co-founder, Owen Carr, previously built reconciliation systems at a large bank. Together, their experience connects the day-to-day burden of exceptions with the systems used to reconcile them. LedgerLark’s wager is that this combination can produce a tool suited to firms whose operations teams may not have the resources of a large institution. The anecdote explains the starting point, but not yet the size of the opportunity. LedgerLark will still need to show that it can handle a range of exceptions, not just the kind that inspired the product. ## Proposals, with a human deciding LedgerLark’s agents compare confirmations, custodian records and internal ledgers. When the records differ, the software proposes corrections for human approval. It does not execute trades or move money. That boundary is central to the product’s pitch. The agents are meant to gather and compare information and suggest a next step, while staff retain the decision to approve a change. It also leaves important tests ahead: how clearly proposals are explained, how often reviewers reject them, and whether the software saves time once setup and oversight are included. Those details will matter as much as the appeal of adding AI to an established workflow. ## Early paying customers, limited evidence Five firms are paying customers, with annual contracts ranging from $36,000 to $110,000. That is evidence of willingness to pay, though it is a small base from which to judge retention or repeatability. LedgerLark says one customer reduced its unresolved daily exceptions from 70 to 18. If sustained, that would be a substantial operational improvement. The figure is company-reported, however, and one customer’s experience does not establish typical results. It is also not clear from the figure alone how exception volume, staffing or the mix of cases changed. The company has raised a $3.5 million seed round led by Motive Row. The funding gives it room to develop the product and pursue more customers; it does not resolve the commercial and technical questions ahead. ## A cautious buying environment LedgerLark is not entering an empty market. It faces established reconciliation vendors as well as software built internally by banks. Its focus on smaller investment firms may help distinguish it, but prospective customers can reasonably ask why an agent-based approach is better than improving an existing reconciliation system. Sales may also move slowly. Financial institutions can require lengthy security and procurement reviews, which can test a young company’s capacity and delay expansion beyond its first customers. For now, LedgerLark offers a specific, testable proposition: reduce the effort of investigating trade mismatches while leaving corrections subject to human approval. The next useful evidence will be less about the label “AI agent” and more about consistent results across customers, implementations and reviews.

Qwen: Qwen3.8 2.4T A95B: # Startup Spotlight: LedgerLark Tackles Post-Trade Operations With AI Agents For many small investment firms, the work that follows a trade can be as stubborn as the market itself. Confirmations arrive in different formats, custodian records do not always line up, and internal ledgers need to be checked line by line. LedgerLark, an early-stage startup, is trying to automate part of that process with AI agents built for post-trade operations. The idea came from a familiar operational failure. Co-founder Mina Batra spent seven years in operations at a regional brokerage, where she saw how small errors could consume disproportionate time. In one episode that later shaped the company’s direction, a six-person team spent three days resolving a trade mismatch caused by a copied account number. The problem was not exotic. It was routine, tedious and expensive in terms of staff hours. Batra co-founded LedgerLark with Owen Carr, who had worked on reconciliation systems at a large bank. Their combined backgrounds point to a practical insight: much of reconciliation is not about making high-stakes judgments, but about gathering documents, comparing records and identifying patterns that usually point toward a fix. That is the kind of work software can help organize, even if humans still need to approve the final answer. LedgerLark’s agents compare confirmations, custodian records and internal ledgers, then propose corrections for human approval. The company emphasizes a limited scope: the product does not execute trades or move money. Instead, it surfaces discrepancies, suggests how they might be resolved and leaves the final decision to operations staff. That design choice matters. Financial institutions are often cautious about letting software take direct action, especially where client assets or regulatory records are involved. By positioning the agents as a support layer rather than an autonomous decision-maker, LedgerLark may make adoption easier while still addressing a time-consuming workflow. Early traction is modest but visible. The company says five firms are paying customers, with annual contracts ranging from $36,000 to $110,000. It also reports that one customer reduced its unresolved daily exceptions from 70 to 18. Because this result comes from the company rather than an independent audit, it should be treated as an early indicator, not proof of broad effectiveness. Still, for a small operations team, a drop of that kind could mean fewer late-night escalations and less manual rechecking. The startup has also raised a $3.5 million seed round led by Motive Row. Funding can help LedgerLark refine its product, meet security requirements and support customers through integration, but the hard work is likely to be in execution. Post-trade systems touch sensitive data, and financial institutions may require lengthy security and procurement reviews before signing contracts. Competition is another open question. LedgerLark is not entering an empty field. Established reconciliation vendors already serve parts of the market, and some banks rely on internal software developed over years of operational use. Those systems may be older or less flexible, but they have the advantage of institutional familiarity. A newer AI-based tool will need to show that it can reduce errors and save time without creating fresh risks or integration headaches. The broader opportunity seems plausible. Small investment firms may lack the budget to build large bespoke operations teams, yet they still face many of the same reconciliation burdens as bigger institutions. If LedgerLark can prove that its agents are reliable, explainable and easy to deploy, it may find a niche among firms that need more automation but cannot afford extensive custom development. For now, the company’s challenge is to turn early promise into repeatable results. The first customers, the seed funding and the founders’ operational experience all give it a starting point. What remains to be seen is whether LedgerLark can move from useful exception-handling tool to a trusted part of the daily post-trade routine.