
SalesTarget.ai — AI-Powered Sales Intelligence Operating System
Find & Enrich with 840M+ profiles. Validate contacts. Reach buyers on Email and LinkedIn. Close with a CRM built for salespeople — Power Dialer included.
SalesTarget.ai is a Sales OS built for outbound-driven B2B companies, agencies, and modern revenue teams. It centralizes every stage of the sales workflow — from data intelligence and enrichment to outreach, pipeline management, and AI assistance — eliminating the need for multiple disconnected tools.
At its core, the Intelligence Engine delivers prospecting power via 840M+ profiles, 150M+ company entities, 4,000+ data signals, and 50+ premium data providers — including real-time intent signals that surface in-market buyers before your competitors do.
Key capabilities:
Cold Email Outreach — smart sending, warm-up sequences, spintax & unified inbox
Power Dialer — auto-sequential dialing directly from the CRM
LinkedIn Automation — connection requests, InMail & multichannel drip sequences
Built-in Email Validation — reduce bounces & protect sender reputation
Integrated CRM — pipeline, deals, call logs, tasks & team collaboration
AI Co-pilot — find leads, build sequences & launch campaigns via simple chat commands
Intelligence → Enrichment → Validation → Email → Power Dialer → LinkedIn → CRM → AI Co-pilot. One platform. Infinite scale.
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Elecard Boro is a professional software solution designed to monitor video stream health and track QoS/QoE parameters across distributed networks. By providing centralized access to statistics and automated reporting, Boro enables telecom professioals to build a powerful monitoring ecosystem from scratch or easily scale existing infrastructure to ensure flawless broadcast quality.
How it works:
Boro utilizes distributed software probes to monitor UDP, RTP, HTTP, HLS, DASH, SRT, and RTMP streams. By aggregating multi-point measurements on a centralized server, operators can instantly isolate quality degradation across the entire delivery chain. The platform provides network-wide visibility and real-time alerts for ETSI TR 101 290 errors via Email, SNMP, Webhook, PagerDuty, and Telegram.
Key Features:
• Rapid Deployment & Scalability: Launch a monitoring probe in just 30 minutes. Easily scale your infrastructure by adding new probes to the unified Boro ecosystem on any hardware.
• Proactive Issue Resolution: Monitor over 50 QoS and QoE parameters (including full ETSI TR 101 290 compliance) and use triggers to localize network anomalies before they impact viewers.
• Advanced Diagnostics: Use comprehensive analysis of SCTE-35 ad-insertion cues and PCAP stream recording for in-depth delivery troubleshooting.
• Effortless Integration & Access: Access monitoring data from any device via an intuitive web interface. Seamlessly integrate Boro into your existing workflow using WebHook, SNMP, and ControlAPI.
• Operational Efficiency: Reduce the workload on QA and network engineers through automated regular reporting, advanced visualization dashboards, and smart threshold tuning that eliminates false alarms.
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Kimi K3
Kimi K3 is a large-scale AI model from Moonshot AI designed for advanced reasoning, software engineering, visual understanding, agentic workflows, and knowledge work. The model is built with 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention design created to support long-context intelligence. It also includes Attention Residuals and a native 1 million token context window, giving developers room to work with large files, repositories, documentation sets, transcripts, and enterprise knowledge bases. Kimi K3 always runs with thinking mode enabled and currently supports maximum reasoning effort by default. Developers can access the model through Moonshot’s OpenAI-compatible API using Python, cURL, and the OpenAI SDK. The API supports standard chat completions, streaming output, structured JSON Schema responses, partial continuation from a prefix, custom tool calling, required tool choice, and dynamic tool loading. Kimi K3 also supports vision inputs, including local images encoded as base64 and video files uploaded through the file API. Automatic context caching helps repeated long-prefix workflows become more efficient without requiring manual cache IDs or extra cache parameters. By combining long context, visual understanding, tool use, structured output, and advanced reasoning, Kimi K3 is built for developers creating sophisticated AI agents, coding systems, research tools, and enterprise applications.
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GLM-5.3
GLM-5.3 is Z.ai’s advanced coding and agentic reasoning model built through scaled post-training on top of the GLM-5.2 base model. The release focuses on frontier coding, long-horizon software engineering, agent tasks, cyber evaluation, and reinforcement learning at scale. GLM-5.3 improves significantly over GLM-5.2 on complex coding benchmarks, real-world engineering environments, Terminal Bench 3.0, DeepSWE, Agents’ Last Exam, and Z.ai’s internal Code Bench. The model is trained on environments that resemble real professional work, including tasks involving codebases, infrastructure, documentation, compute clusters, experiments, bottleneck diagnosis, implementation, testing, and measurable optimization. Z.ai’s post-training stack includes IndexShare for efficient long-context processing, SAO for reinforcement learning on long-horizon tasks, and slime for large-scale asynchronous training. GLM-5.3 supports three thinking effort levels, including low, high, and max, with max recommended for coding tasks. The model also demonstrates emergent cyber capabilities across vulnerability discovery and exploitation benchmarks, prompting continued safety evaluation and hardening before weights are released. GLM-5.3 can be used through the GLM Coding Plan, ZCode, Claude Code, OpenCode, and other coding agent workflows. By combining stronger coding performance, long-horizon task execution, post-training scale, cyber evaluation, reasoning effort controls, and coding-agent integrations, GLM-5.3 supports advanced developer and research workflows.
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