Agregátor RSS
Tengu Botnet Reboots Compromised Linux Devices When Defenders Kill Its Process
Mak's Weekly Security Roundup: Linux Security Priorities This Week
Paměťová krize nekončí, nově zdraží i procesory. Výrobci topmodelů by si ale mohli pomoci microSD kartami
24,650 Internet-Exposed BMCs Disclose IPMI Password Hashes Before Login
Is Your SSO Protected Against Modern Credential Attacks?
Synsira Launches Kind Local Pro with 100% On-Device AI
Operating entirely offline, Kind Local Pro gives individuals local data sovereignty without sacrificing AI performance
Synsira Software is addressing the biggest concern with AI: privacy. Today, the company introduced Kind Local Pro, an AI platform that operates independently of corporate cloud-based LLM models and is available to download onto desktops and laptops. Designed for people and organizations wanting the benefits of AI without sending their data, analysis and queries to external sources, the platform allows users to take control of their information.
Kind Local Pro builds on Synsira’s flagship Kind platform, giving professionals, researchers, communicators, legal teams, educators and organizations a more private way to search and understand their own files using AI. Built to operate locally on a user’s desktop or laptop, Kind Local Pro lets users create collections and ask questions across their materials, with answers generated only from their data and not the open web.
“People want AI to be useful, but they also want to know where their data is going,” said Dr. Jonathan Schaeffer, founder of Synsira Software and creator of Kind. “That is not a small concern. For many people and businesses, it is the whole issue. Kind Local Pro was built for users who want AI on their own terms: private, local and grounded in their own information.”
With Kind Local Pro, users add documents, presentations, research papers, notes, videos, images, email inboxes, audio and other supported files into collections. Once the content is indexed, summarized, tagged and analyzed by Kind AI, they can ask natural-language questions or do fuzzy searching and receive answers with precise citations into the information in those files. If the user’s data does not contain enough information to answer a question, Kind Local Pro is designed to say so rather than invent a response.
The platform is purpose-built for data-sensitive environments:
- Legal and Compliance: Lawyers can analyze internal memoranda and client files with all analysis private and staying local to their machine
- Intellectual Property: Agency professionals, influencers, creators and executives can organize proprietary brand assets, manuscripts and corporate strategies with zero risk of their data being used to train public models.
- Academic Research: Scientists and researchers can search years of papers, drafts and video lecture materials while keeping unpublished work on their own machine.
The product reflects Synsira’s broader view that AI adoption depends on trust, transparency and practical value. Many people remain cautious about AI because of concerns about errors or bias in internet answers, privacy, data training, security and the environmental demands of large-scale cloud computing. Kind Local Pro addresses those concerns by moving the AI experience closer to the user and keeping private data under local control.
“Not every AI task needs to be sent to a massive data center,” said Schaeffer. “Sometimes the smartest place for AI to work is right where the information already lives: on your own computer. Bigger is not always better. Private, practical and accurate is better.”
Kind Local Pro allows for 10,000 files to be uploaded, up to 500 at a time. It is now available for an initial subscription cost of $79 at Kind.Synsira.com. Local Kind Free allows up to 50 files to be uploaded. Both versions are 1.9GB installed plus 6GBs of AI models installed.
A media kit with logos, headshots and screenshots of Kind Local Pro is available here.
About Kind by Synsira
Synsira builds ethical, user-friendly Al products from rigorously evaluated and curated Al models for folks who demand privacy and environmental responsibility in Al. Synsira’s flagship product, Kind, available now at synsira.com, is a desktop Al application that securely and privately helps users unlock the knowledge contained in their own curated data. By putting guardrails on the Al commercial and open-source models and implementing strict data controls, Kind Al delivers accurate, reliable results with surgical precision across all personal files, photos, video and audio.
ContactBethany Rhodes
Bank for charities pulls online services over security fears
Linux Logs Have Become a Prompt Injection Target
Chytré semafory s umělou inteligencí zrychlí dopravu a spravedlivěji rozdělí čas mezi řidiče i chodce
JFrog Confirms OpenAI Models Exploited Artifactory Zero-Day Before Hugging Face Breach
Critical OpenWrt DHCPv6 Flaw Could Let Unauthenticated Attackers Run Code as Root
Uncle Sam needs you to fight for 6G leadership and security, lest Beijing get there first
Internet získá novou doménu .web. Spravovat ji bude organizace stojící za .com
Česká národní kvantová komunikační infrastruktura
Šumperk čelil kybernetickému útoku, úřad omezil provoz, únik dat se prověřuje
Over 24,000 exposed server BMCs leak password hash via decades-old flaw
Samba 4.24.5, 4.23.10 a 4.22.11
Nimbus Manticore Deploys NightLedger and Turns Victim Systems Into Covert Relays
Připojili jste se k palubní Wi-Fi Českých drah, ale internet nefunguje? Zkuste tohle
Anthropic rejects open-weight AI bans, calls for China chip controls and safety tests
Anthropic CEO Dario Amodei has argued that policymakers should keep lower-risk open-weight AI accessible while placing stricter safeguards around frontier systems, including mandatory testing and limits on China’s access to advanced computing and model capabilities.
In a post outlining Anthropic’s position, Amodei said broad restrictions, including bans on Chinese open-weight models used by US businesses, would not address his main national security concerns. Instead, he pointed to the possibility of authoritarian governments surpassing the US in advanced AI, as well as cyber, biological, and alignment risks posed by increasingly capable systems.
Amodei also called for action against industrial-scale model distillation, which he said allows Chinese developers to improve their models with less computing power than would be needed to train comparable systems from scratch.
The statement followed criticism of Anthropic for not signing an industry letter backed by Nvidia, Microsoft, Meta, IBM, Mistral, Hugging Face and other technology companies urging policymakers to avoid premature restrictions on open-weight models.
The letter said that open weights could broaden access to AI, intensify competition, and enable organizations to adapt and deploy models without relying on a single provider. Amodei agreed with parts of that case but disputed claims that openness inherently improves safety research or gives defenders an advantage over attackers.
He said regulation should be based on a model’s capabilities and risks rather than whether its weights are openly available. Under that approach, sufficiently capable open and closed models would undergo testing before release.
Conditional supportAnalysts said Anthropic had moved closer to industry consensus by rejecting blanket bans, but its support remained more limited than the approach backed by many major technology companies.
Deepika Giri, head of research for AI, analytics, and data at IDC, said the Nvidia-backed letter presented open weights as strategic infrastructure that should remain broadly accessible, in contrast with Anthropic’s more restrictive position.
Amodei’s statement clarified that Anthropic supports open-weight models only under certain conditions, a stance that could also help the company preserve its competitive advantages as a proprietary model provider focused on compliance and tighter controls, according to Lian Jye Su, chief analyst at Omdia.
The statement was “a real olive branch” to supporters of open-weight models, according to Pareekh Jain, CEO of Pareekh Consulting. But he said the disagreement had shifted from whether such models should be released to where policymakers should draw the line.
“Anthropic still thinks that once a model gets powerful enough, releasing its weights publicly is riskier than keeping it locked behind an app, because you can never take it back or add safety fixes later,” Jain said.
Will the controls work?Analysts differed over whether Anthropic’s proposed controls would achieve their aims without creating new barriers for smaller AI developers.
Jain said chip restrictions and measures against illicit model distillation would mainly affect model developers and infrastructure providers, rather than enterprises using models already on the market. Mandatory safety testing, however, could raise development costs and reduce the number of advanced open-weight models available.
“Testing is expensive and time-consuming, and so, giant, well-funded companies like Anthropic, Google and OpenAI can afford it,” Jain said. Smaller developers seeking to release cutting-edge open-weight models could struggle to meet the same requirements, he added.
The additional testing and screening could also restrict the number of open-weight models available to enterprises, according to Su. He said the requirements could weaken some of their principal benefits, including lower costs, reduced vendor dependence and community-led development.
Anand Joshi, managing director of market research firm JP Data, questioned whether limiting China’s access to advanced chips would materially slow its AI development, arguing that Chinese companies had shown they could build highly capable models with less computing power. He supported action against illicit distillation, however, saying safeguards were needed to prevent developers from reproducing the capabilities of other models without authorization.
The impact on most enterprise users could remain limited if less capable models were exempted, Jain said. Businesses deploying models that fall below the proposed testing threshold would probably face little additional cost.
How CIOs should chooseGiri said CIOs should assess models according to their capabilities rather than whether they are open, and should demand independent testing, clear licensing, model documentation and accountability for monitoring and incident response.
“Mandatory safety testing should be triggered by a model’s demonstrated capabilities, not its size or training cost,” Jain said, particularly when a system could significantly assist cyberattacks, biological misuse, or autonomous harmful actions.
Before deployment, CIOs should seek independent evaluations, detailed model documentation, security test results and information about the model’s software supply chain, he added. Charlie Dai, principal analyst at Forrester, said that assessment should include documented red-team results, model provenance, disclosures about training and fine-tuning, and evidence of independent testing against recognized safety benchmarks.
- « první
- ‹ předchozí
- …
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- …
- následující ›
- poslední »



